Universal Demosaicking of Colour Filter Arrays

Chao Zhang, Yan Li, Jue Wang, and Pengwei Hao

We recently have developed a universal demosaicking method, which can be used for all Colour Filter Arrays, periodic or aperiodic, regular or irregular, uniform or nonuniform. It is non-iterative, and can be linear (image-independent) or nonlinear (image-dependent), so it is good for CFA demosaicking, design and comparison.

You can download our MATLAB code here:

http://www.eecs.qmul.ac.uk/~phao/CFA/acude/acude.zip

which is only for demonstration of our acude method for the Bayer CFA pattern, and are implemented naively, thus they may run slowly. If you wish us to test our method on your CFA patterns, we are also happy to do it. Please contact us.

Actually the algorithm can be implemented very fast, which can be about 1000 times faster for linear demosaicking and 8000 times faster for both linear and adaptive (acude). The comparison is based on MATLAB code only, so it can be even much faster with C, C++ or with the help of some hardware. Please contact Pengwei Hao for further information: p.hao@qmul.ac.uk. The details of the times for images of size 500x500 are listed below for comparison:

Times Table: CPU times to demosaick 500x500 sized images with MATLAB for comparison between our acude codes of slow and fast versions

	slow acude (CPU time in seconds)			fast acude (CPU time in seconds)			gains (time_slow/time_fast)
demosaic	linear	adaptive	acude	linear	adaptive	acude	linear	adaptive	acude
mean	360.58	18637.46	19002.19	0.388	2.060	2.507	931.72	9053.98	7584.75
min	358.97	18411.64	18776.17	0.328	1.934	2.402	852.26	8607.78	7229.81
max	363.08	19424.09	19783.67	0.421	2.168	2.621	1101.52	9577.89	8077.55

The programmes are run on MATLAB R2013b. The computer is a PC with 8G RAM and the OS is Windows 7.

The Processor is: Intel(R) Core(TM) i5-2500K CPU @ 3.30GHz, 3301 Mhz, 4 Core(s), 4 Logical Processor(s).

MATLAB function cputime was used for the time measurement, so wall clock times should be only 1/4 of the times in the table.

If the code or the method is used for research, please cite:

Chao Zhang, Yan Li, Jue Wang, and Pengwei Hao, "Universal Demosaicking of Color Filter Arrays", IEEE Transactions on Image Processing, Vol. 25, No. 11, pp. 5173-5186, Nov 2016.

DOI: 10.1109/TIP.2016.2601266

Available on http://ieeexplore.ieee.org/document/7547314/

The comparison is highly favourable between our method and other universal demosaicking methods, i.e. Condat's variational method (Var) and Menon&Calvagno's regularization approach (Rad, adaptive) with the Bayer CFA, the Kodak's CFA2.0, Fuji X-Trans, Sony RGBW, our previously proposed CFAs (Hao40, Hao50, Hao60, Hao4x4), a random CFA proposed by Condat and two non-RGBWCMY color CFAs respectively by Hirakawa et al and Condat (See Figures 1&2 below). Note that our method is fast since it is non-iterative and can be implemented with small filters, while Condat's method (Var) is iterative thus is slow and its convergence is highly dependent on the initial estimate, and Menon&Calvagno's (Rad) actually only works for uniform and periodic CFAs thus we only used the first 36x36 sub-pattern of the random CFA for the experiments and it is much slower than our method.

Figure 1: The tested CFAs proposed by others

Figure 2: The tested CFAs proposed by us

The demosaicking results that we have obtained are listed in the tables (See Summary Tables 0 for the averages and the details in the Tables 1-12 below), for colour MSE (CMSE), the corresponding colour PSNR (CPSNR) and the mean CIE Lab error between the demosaicked images and the original images. These results were obtained using 24 test images of Kodak Lossless True Colour Image Suite (http://r0k.us/graphics/kodak/) and 18 test images of the IMAX dataset (http://www4.comp.polyu.edu.hk/~cslzhang/CDM_Dataset.htm). The experiments have been done without mixed noise and with additive noise of standard deviation 5.

Table 0: Averages of the demosaicking performance on Kodak and IMAX datasets with all 11 CFAs

Dataset		Kodak dataset						IMAX dataset
CFA pattern	Demosaicking method	Noise free			Additive Noise (std=5)			Noise free			Additive Noise (std=5)
		CMSE	CPSNR	CIE LAB	CMSE	CPSNR	CIE LAB	CMSE	CPSNR	CIE LAB	CMSE	CPSNR	CIE LAB
Bayer CFA	Var	10.74	38.60	1.515	32.56	33.09	2.767	24.57	35.28	3.621	42.66	32.18	6.720
	Rad	7.96	39.95	1.320	29.89	33.44	2.695	20.89	36.05	3.269	39.92	32.43	6.072
	Ours	6.46	40.84	1.186	29.44	33.49	2.513	19.36	36.38	3.150	39.80	32.41	5.811
Kodak CFA2.0	Var	14.38	37.29	2.146	39.77	32.23	3.364	31.82	34.19	4.764	61.27	30.55	8.859
	Rad	12.42	37.88	1.949	39.81	32.20	3.552	28.99	34.61	4.404	58.46	30.73	7.733
	Ours	10.50	38.70	1.764	37.01	32.52	3.093	25.52	35.15	3.868	54.29	31.04	6.971
Fuji X-Trans	Var	11.58	38.25	1.714	34.34	32.86	2.894	27.23	34.85	3.869	46.41	31.83	6.891
	Rad	10.23	38.83	1.569	32.22	33.14	2.807	24.12	35.39	3.558	43.61	32.07	6.247
	Ours	8.69	39.54	1.437	32.28	33.11	2.628	21.02	35.99	3.276	41.42	32.26	5.729
Sony RGBW	Var	16.82	36.59	2.279	41.41	32.08	3.365	33.96	33.90	4.827	61.07	30.59	8.663
	Rad	14.90	37.19	2.056	40.72	32.14	3.479	29.98	34.46	4.427	57.89	30.79	7.661
	Ours	12.10	38.10	1.861	37.45	32.49	3.007	27.05	34.87	3.976	53.42	31.14	6.688
Random CFA	Var	9.41	39.19	1.527	31.74	33.19	2.766	28.74	34.36	4.036	46.92	31.73	6.926
	Rad	8.34	39.72	1.413	29.95	33.43	2.696	21.19	35.99	3.373	39.68	32.47	6.125
	Ours	7.70	40.10	1.341	30.25	33.38	2.571	19.86	36.25	3.198	38.36	32.60	5.654
Hirakawa 4 color	Var	7.57	39.34	1.606	33.32	32.90	3.135	25.37	34.09	4.261	52.59	30.92	7.452
	Rad	7.68	39.28	1.618	33.14	32.93	3.140	24.87	34.17	4.261	54.71	30.75	7.841
	Ours	6.83	39.79	1.501	33.56	32.87	3.036	24.75	34.20	4.083	54.14	30.80	7.215
Condat 6 color	Var	7.46	39.40	1.569	32.37	33.03	3.089	21.74	34.76	3.987	47.68	31.35	7.322
	Rad	7.52	39.37	1.575	32.10	33.07	3.102	21.28	34.85	4.003	48.40	31.28	7.507
	Ours	6.64	39.91	1.467	31.94	33.09	2.924	18.66	35.42	3.647	44.69	31.63	6.856
Hao4x4	Var	7.94	39.89	1.713	33.59	32.91	3.179	25.27	35.29	4.636	53.79	31.09	8.581
	Rad	7.34	40.22	1.623	32.18	33.10	3.122	23.17	35.64	4.267	52.22	31.19	7.646
	Ours	6.52	40.73	1.524	32.20	33.09	3.013	23.27	35.64	4.049	50.40	31.38	7.115
Hao40	Var	12.48	37.88	1.925	36.08	32.65	3.066	26.34	35.02	4.222	50.26	31.42	7.722
	Rad	11.08	38.44	1.780	33.80	32.93	3.012	23.50	35.53	3.935	48.10	31.58	7.004
	Ours	9.90	38.93	1.652	34.18	32.86	2.849	20.08	36.21	3.467	42.94	32.06	6.115
Hao50	Var	10.96	38.47	1.889	37.49	32.46	3.524	25.27	35.26	4.466	57.73	30.74	9.766
	Rad	8.72	39.55	1.682	35.27	32.71	3.488	21.65	35.96	4.081	54.85	30.94	8.274
	Ours	6.79	40.61	1.496	35.13	32.71	3.415	18.15	36.71	3.841	53.59	31.00	8.186
Hao60	Var	15.79	36.82	2.496	41.34	32.07	3.713	33.47	34.02	5.543	65.55	30.26	10.166
	Rad	15.15	37.02	2.342	40.26	32.19	3.644	29.43	34.59	4.889	62.77	30.41	8.654
	Ours	13.57	37.51	2.119	40.36	32.16	3.461	24.72	35.31	4.338	54.81	30.98	7.687

The tables show that our new universal demosaicking method gives the best performance in average. The performance with Bayer CFA is even better than most of the published methods that were specially designed for the Bayer pattern.

For the demosaicked images, we show in the figure tables (See Figure Table F1-4 below) for the crops of the image 19 of Kodak dataset and image 3 of IMAX dataset.

All the demosaicked images with Kodak and IMAX datasets without and with noise (std=5) are available to download on web:

Demosaicking with Condat's generic variational method (var) :

Kodak (noise free): http://www.eecs.qmul.ac.uk/~phao/CFA/acude/Kodak/var-images/

Kodak (noise std=5): http://www.eecs.qmul.ac.uk/~phao/CFA/acude/Kodak/var-n5-images/

IMAX (noise free): http://www.eecs.qmul.ac.uk/~phao/CFA/acude/IMAX/var-images/

IMAX (noise std=5): http://www.eecs.qmul.ac.uk/~phao/CFA/acude/IMAX/var-n5-images/

Demosaicking with Menon&Calvagno's regularization approach (rad) :

Kodak (noise free): http://www.eecs.qmul.ac.uk/~phao/CFA/acude/Kodak/rad-images/

Kodak (noise std=5): http://www.eecs.qmul.ac.uk/~phao/CFA/acude/Kodak/rad-n5-images/

IMAX (noise free): http://www.eecs.qmul.ac.uk/~phao/CFA/acude/IMAX/rad-images/

IMAX (noise std=5): http://www.eecs.qmul.ac.uk/~phao/CFA/acude/IMAX/rad-n5-images/

Demosaicking with our adaptive universal demosaicking method (acude) :

Kodak (noise free): http://www.eecs.qmul.ac.uk/~phao/CFA/acude/Kodak/acude-images/

Kodak (noise std=5): http://www.eecs.qmul.ac.uk/~phao/CFA/acude/Kodak/acude-n5-images/

IMAX (noise free): http://www.eecs.qmul.ac.uk/~phao/CFA/acude/IMAX/acude-images/

IMAX (noise std=5): http://www.eecs.qmul.ac.uk/~phao/CFA/acude/IMAX/acude-n5-images/

In our view, the big advantages of our new universal demosaicking method are as follows:

1) The demosaicking performance of our method is very good, in terms of the image quality. Based on our experiments, for all the tested CFAs, our method does the best, and Menon&Calvagno's does better than Condat's.

2) Our method can be used for all CFAs, periodic or aperiodic, regular or irregular, uniform or nonuniform.

3) Our method can be fast for its non-iterative characteristic and can be implemented with small filters.

4) Our method can be further optimised easily for a specific given CFA.

5) Our method can be linear or nonlinear, and can be adaptive or non-adaptive to images.

References:

CFA patterns:

[Bayer] B. E. Bayer, "Color imaging array", US Patent 3 971 065, 1976.

[Kodak CFA2.0] T. Kijima, H. Nakamura, J. Compton, and J. Hamilton, "Image sensor with improved light sensitivity", US Patent 20070268533, 22 Nov 2007.

[Fuji X-Trans] http://fujifilm-x.com/x-pro1/en/about/sensor/index.html

[Sony RGBW] I. Hirota, "Solid-state imaging device, method for processing signal of solid-state imaging device, and imaging apparatus", Dec. 4 2009, US Patent App. 12/630,988.

[Random] L. Condat, "Color filter array design using random patterns with blue noise chromatic spectra", Image and Vision Computing, vol. 28, no. 8, pp. 1196-1202, Aug. 2010.

[Hao4x4] P. Hao, Y. Li, Z. Lin, and E. Dubois, "A geometric method for optimal design of color filter arrays", IEEE Transactions on Image Processing, Vol. 20, No. 3, pp. 709-722, March 2011.

[Hao40] J. Wang, C. Zhang, and P. Hao, "New Color Filter Arrays of High Light Sensitivity and High Demosaicking Performance", IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, Sept 11-14, 2011.

[Hao50, Hao60] P. Hao, "Colour Filter", UK Patent Application 1101288.7.

[Hirakawa 4 color] K. Hirakawa, and P.J. Wolfe, "Spatio-spectral color filter array design for optimal image recovery", IEEE Transactions on Image Processing, vol. 17, no. 10, pp. 1876-1890, October 2008.

[Condat 6 color] L. Condat, "A new color filter array with optimal properties for noiseless and noisy color image acquisition", IEEE Transactions on Image Processing, vol. 20, no. 8, pp. 2200-2210, August 2011.

Universal demosaicking methods:

[Var] L. Condat, "A generic variational approach for demosaicking from an arbitrary color filter array", IEEE International Conference on Image Processing, pp. 1625-8, Nov 7-10 2009, Cairo, Egypt.

[Rad] D. Menon, and G. Calvagno, "Regularization approaches to demosaicking", IEEE Transactions on Image Processing, vol. 18, no. 10, pp. 2209-2210, October 2009.

[Ours Acude] Chao Zhang, Yan Li, Jue Wang, and Pengwei Hao, "Universal Demosaicking of Color Filter Arrays", IEEE Transactions on Image Processing, Vol. 25, No. 11, pp. 5173-5186, Nov 2016.

Figure Table F1. Demosaicked image region 1 of Lighthouse (Kodak image 19)

Noise	Noise Free			Noise std=5
Demosaicking Method	Condat's generic variational approach (Var)	Menon&Calvagno's regularization approach (Rad)	Our adaptive universal demosaicking (acude)	Condat's generic variational approach (Var)	Menon&Calvagno's regularization approach (Rad)	Our adaptive universal demosaicking (acude)
Original image
Bayer CFA
Kodak CFA2.0
Fuji X-Trans
Sony RGBW
Random
Hirakawa 4 color
Condat 6 color
Hao4x4
Hao40 (Our RGBW)
Hao50
Hao60

Figure Table F2. Demosaicked image region 2 of Lighthouse (Kodak image 19)

Noise	Noise Free			Noise std=5
Demosaicking Method	Condat's generic variational approach (Var)	Menon&Calvagno's regularization approach (Rad)	Our adaptive universal demosaicking (acude)	Condat's generic variational approach (Var)	Menon&Calvagno's regularization approach (Rad)	Our adaptive universal demosaicking (acude)
Original image
Bayer CFA
Kodak CFA2.0
Fuji X-Trans
Sony RGBW
Random
Hirakawa 4 color
Condat 6 color
Hao4x4
Hao40 (Our RGBW)
Hao50
Hao60

Figure Table F3. Demosaicked image region 1 of IMAX image 3

Noise	Noise Free			Noise std=5
Demosaicking Method	Condat's generic variational approach (Var)	Menon&Calvagno's regularization approach (Rad)	Our adaptive universal demosaicking (acude)	Condat's generic variational approach (Var)	Menon&Calvagno's regularization approach (Rad)	Our adaptive universal demosaicking (acude)
Original image
Bayer CFA
Kodak CFA2.0
Fuji X-Trans
Sony RGBW
Random
Hirakawa 4 color
Condat 6 color
Hao4x4
Hao40 (Our RGBW)
Hao50
Hao60

Figure Table F4. Demosaicked image region 2 of IMAX image 3

Noise	Noise Free			Noise std=5
Demosaicking Method	Condat's generic variational approach (Var)	Menon&Calvagno's regularization approach (Rad)	Our adaptive universal demosaicking (acude)	Condat's generic variational approach (Var)	Menon&Calvagno's regularization approach (Rad)	Our adaptive universal demosaicking (acude)
Original image
Bayer CFA
Kodak CFA2.0
Fuji X-Trans
Sony RGBW
Random
Hirakawa 4 color
Condat 6 color
Hao4x4
Hao40 (Our RGBW)
Hao50
Hao60

Tables 1-12: Details of demosaicking performance- CMSE, CPSNR & CIE LAB error

Table 1: Demosaicking performance (CMSE) on Kodak dataset (noise free) with all 11 CFAs

CMSE	Bayer CFA			Kodak CFA2.0			Fuji X-Trans			Sony RGBW			Random CFA			Hirakawa 4 color			Condat 6 color			Hao4x4			Hao40			Hao50			Hao60
Image	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours
1	12.91	9.57	7.20	13.82	12.99	10.10	11.56	12.38	9.35	18.05	20.80	13.02	8.66	9.07	7.59	6.90	7.46	6.08	7.03	7.58	5.26	6.84	7.74	6.11	12.46	13.16	10.41	13.27	10.67	7.10	15.60	17.01	15.17
2	8.11	6.52	4.62	9.99	9.51	7.22	8.52	7.71	6.15	12.26	10.43	8.56	7.03	6.64	5.54	6.29	6.89	5.56	6.12	6.74	5.34	6.26	6.18	5.59	9.00	8.24	6.98	8.34	6.56	5.19	10.45	9.20	10.08
3	5.44	3.55	3.27	7.82	6.77	5.05	6.67	4.91	4.45	10.03	6.33	6.35	5.51	4.24	3.89	4.21	4.21	3.56	3.95	3.84	3.57	4.96	3.92	3.47	8.86	6.41	6.02	5.77	3.85	3.25	12.34	10.46	9.66
4	6.29	5.00	4.89	10.14	8.95	8.75	7.70	6.51	6.73	12.33	9.40	10.74	6.21	5.44	5.69	5.67	5.80	6.05	5.43	5.49	5.68	5.36	4.88	5.05	8.75	7.26	7.82	7.10	5.13	5.74	10.18	8.95	12.48
5	13.14	9.32	8.39	27.68	21.43	19.45	18.95	14.82	13.75	33.34	21.78	22.77	15.06	11.86	12.24	13.26	12.65	12.45	12.74	12.16	11.88	12.64	10.63	9.87	21.38	16.59	16.06	14.02	10.72	9.03	27.78	23.82	22.56
6	10.30	6.72	4.84	11.27	9.88	8.26	9.51	8.73	6.55	12.95	14.69	9.05	7.13	6.50	5.67	5.41	5.46	4.50	5.49	5.39	4.20	5.90	5.61	4.63	9.25	9.01	7.52	10.17	7.44	4.81	11.43	12.41	10.53
7	4.79	3.23	2.90	8.67	7.46	6.06	5.99	4.80	4.34	10.16	7.26	6.95	4.81	4.03	3.81	4.38	4.44	4.08	4.17	4.10	4.15	4.47	3.92	3.54	6.98	5.49	5.19	5.47	4.00	3.33	9.85	8.33	7.84
8	32.37	16.62	10.72	23.69	21.07	12.84	19.49	18.84	11.88	29.50	42.52	14.17	14.15	13.50	9.80	11.62	11.96	9.06	11.39	11.73	8.49	11.78	11.73	9.20	18.78	18.35	12.63	32.65	18.34	10.11	23.72	24.33	17.33
9	5.29	3.59	2.70	7.18	6.34	4.61	5.44	4.71	3.67	8.22	7.16	5.04	4.41	3.90	3.24	4.04	4.10	3.37	3.81	3.87	3.18	4.10	3.75	3.15	5.91	5.03	4.14	5.61	4.20	2.98	8.35	7.68	5.61
10	4.40	3.52	3.07	7.26	6.23	5.14	5.35	4.56	4.05	7.45	6.16	5.50	4.44	3.75	3.52	4.18	4.05	3.71	3.64	3.65	3.18	3.80	3.37	3.15	7.44	5.71	5.53	4.58	3.84	3.16	9.74	8.31	6.66
11	8.96	6.75	5.05	12.45	11.00	8.75	9.81	8.91	7.11	14.30	13.26	9.88	7.56	7.04	6.18	6.29	6.35	5.42	6.07	6.04	5.20	6.18	6.09	5.30	10.23	9.60	8.15	9.04	7.24	5.51	13.09	13.25	11.58
12	4.12	2.81	2.41	4.86	4.60	3.61	4.03	3.62	3.10	6.00	5.81	4.30	3.16	2.84	2.61	2.75	2.92	2.53	2.65	2.76	2.35	2.79	2.62	2.32	4.39	3.83	3.40	4.40	3.03	2.46	5.41	5.25	5.18
13	21.67	22.48	17.20	35.74	33.57	27.91	31.43	31.28	24.86	40.77	37.86	32.77	25.65	25.17	21.95	18.70	19.11	15.63	19.55	19.79	15.04	20.57	21.59	16.82	32.18	33.29	27.13	22.25	25.49	18.25	40.62	44.05	36.08
14	22.80	14.57	12.17	34.72	27.02	24.40	25.12	18.84	18.04	40.71	27.08	28.43	20.13	15.76	16.14	17.75	17.89	15.85	16.63	16.41	16.81	18.83	15.61	15.53	28.72	21.53	22.96	23.26	15.90	14.34	38.88	31.08	33.48
15	7.43	6.45	6.28	11.53	10.51	10.00	9.58	8.35	8.59	13.15	10.89	11.52	7.71	6.85	7.15	6.85	7.12	7.33	6.63	6.87	6.81	6.76	6.25	6.37	10.67	9.46	9.67	8.13	6.57	6.63	13.27	12.63	13.94
16	5.27	2.97	2.09	4.80	4.45	3.55	4.24	3.96	2.91	5.91	7.35	3.91	3.09	2.93	2.55	2.31	2.39	2.06	2.20	2.21	1.86	2.56	2.56	2.16	4.51	4.44	3.56	5.29	3.32	2.14	5.95	6.79	4.91
17	4.99	4.48	3.80	7.48	6.44	5.73	6.54	5.84	5.07	8.71	6.76	6.27	5.74	5.02	4.80	4.35	4.26	3.81	4.30	4.22	3.82	4.53	4.16	3.64	6.82	6.04	5.53	4.93	4.78	4.02	8.31	8.20	6.72
18	13.11	12.15	10.12	20.86	18.38	16.78	17.16	15.70	13.96	22.05	18.29	18.79	15.03	13.87	13.04	12.64	12.63	11.36	13.49	13.53	12.44	12.89	12.16	10.64	17.96	16.57	15.23	13.52	13.40	11.31	22.51	21.41	19.65
19	10.45	6.27	4.38	9.46	8.09	6.10	7.58	7.11	5.29	11.33	13.72	6.84	5.95	5.63	4.61	4.72	4.79	4.05	4.70	4.80	3.87	4.89	4.83	4.13	7.41	7.23	5.68	10.46	6.92	4.54	9.15	9.52	7.59
20	6.44	5.07	4.06	9.22	7.91	6.05	7.73	6.49	5.33	10.48	8.03	6.81	6.35	5.44	4.74	5.19	5.17	4.14	4.94	4.91	4.25	5.39	4.74	4.03	8.82	7.42	6.04	6.09	5.40	3.98	11.58	11.27	7.47
21	9.82	7.73	6.02	13.54	11.35	9.90	9.97	9.55	7.66	14.96	15.16	10.76	8.10	7.63	6.81	6.23	6.30	5.37	6.21	6.23	5.12	6.53	6.46	5.45	10.16	9.84	8.48	9.65	8.37	5.99	12.89	13.45	11.33
22	12.22	9.26	8.13	16.10	13.61	12.74	12.90	10.91	10.58	18.23	15.20	14.24	11.34	9.66	9.50	8.56	8.68	8.75	8.50	8.58	8.42	9.41	8.18	8.01	13.30	11.32	11.36	12.45	10.08	8.74	16.19	14.73	15.11
23	4.97	3.34	2.80	8.33	6.91	5.87	5.66	4.38	4.09	10.01	6.49	6.96	4.50	3.70	3.76	4.15	4.31	3.82	3.86	4.09	3.91	4.27	3.65	3.51	6.52	4.94	4.99	5.31	3.79	3.32	9.54	7.98	7.98
24	22.39	19.10	17.84	28.50	23.69	23.03	26.96	22.71	21.14	32.82	25.14	26.84	24.05	19.74	19.97	15.20	15.29	15.31	15.52	15.44	14.52	18.86	15.59	14.72	29.08	25.10	23.06	21.28	20.14	16.94	32.04	33.52	26.67
mean	10.74	7.96	6.46	14.38	12.42	10.50	11.58	10.23	8.69	16.82	14.90	12.10	9.41	8.34	7.70	7.57	7.68	6.83	7.46	7.52	6.64	7.94	7.34	6.52	12.48	11.08	9.90	10.96	8.72	6.79	15.79	15.15	13.57

Table 2: Demosaicking performance (CPSNR) on Kodak dataset (noise free) with all 11 CFAs

CPSNR	Bayer CFA			Kodak CFA2.0			Fuji X-Trans			Sony RGBW			Random CFA			Hirakawa 4 color			Condat 6 color			Hao4x4			Hao40			Hao50			Hao60
image	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours
1	37.02	38.32	39.56	36.73	36.99	38.09	37.50	37.21	38.42	35.57	34.95	36.98	38.75	38.56	39.33	39.74	39.40	40.29	39.66	39.33	40.92	39.78	39.24	40.27	37.18	36.94	37.96	36.90	37.85	39.62	36.20	35.82	36.32
2	39.04	39.99	41.48	38.13	38.35	39.55	38.83	39.26	40.24	37.25	37.95	38.80	39.66	39.91	40.69	40.15	39.75	40.68	40.26	39.84	40.86	40.17	40.22	40.66	38.59	38.97	39.69	38.92	39.96	40.98	37.94	38.49	38.10
3	40.77	42.63	42.99	39.20	39.83	41.10	39.89	41.22	41.65	38.12	40.12	40.10	40.72	41.86	42.24	41.89	41.89	42.61	42.16	42.28	42.61	41.18	42.19	42.73	38.66	40.06	40.34	40.52	42.28	43.01	37.22	37.94	38.28
4	40.15	41.14	41.24	38.07	38.61	38.71	39.27	39.99	39.85	37.22	38.40	37.82	40.20	40.77	40.58	40.59	40.50	40.31	40.78	40.74	40.59	40.84	41.25	41.10	38.71	39.52	39.20	39.62	41.03	40.54	38.05	38.61	37.17
5	36.94	38.44	38.89	33.71	34.82	35.24	35.35	36.42	36.75	32.90	34.75	34.56	36.35	37.39	37.25	36.91	37.11	37.18	37.08	37.28	37.38	37.11	37.87	38.19	34.83	35.93	36.07	36.66	37.83	38.57	33.69	34.36	34.60
6	38.00	39.86	41.28	37.61	38.18	38.96	38.35	38.72	39.97	37.01	36.46	38.56	39.60	40.00	40.59	40.80	40.76	41.60	40.73	40.81	41.90	40.43	40.64	41.48	38.47	38.59	39.37	38.06	39.42	41.31	37.55	37.19	37.91
7	41.33	43.04	43.51	38.75	39.40	40.31	40.36	41.31	41.76	38.06	39.52	39.71	41.31	42.07	42.33	41.71	41.66	42.03	41.93	42.01	41.95	41.63	42.19	42.64	39.69	40.73	40.98	40.75	42.11	42.91	38.19	38.93	39.19
8	33.03	35.93	37.83	34.39	34.89	37.05	35.23	35.38	37.38	33.43	31.85	36.62	36.62	36.83	38.22	37.48	37.35	38.56	37.56	37.44	38.84	37.42	37.44	38.50	35.39	35.49	37.12	32.99	35.50	38.08	34.38	34.27	35.74
9	40.89	42.58	43.82	39.57	40.11	41.49	40.77	41.40	42.49	38.98	39.58	41.11	41.69	42.22	43.02	42.06	42.00	42.85	42.33	42.25	43.11	42.01	42.39	43.15	40.41	41.12	41.96	40.64	41.90	43.39	38.91	39.28	40.64
10	41.70	42.67	43.26	39.52	40.19	41.02	40.85	41.54	42.06	39.41	40.23	40.73	41.66	42.39	42.66	41.92	42.06	42.43	42.52	42.51	43.11	42.33	42.86	43.14	39.42	40.57	40.71	41.53	42.29	43.13	38.24	38.94	39.89
11	38.61	39.84	41.10	37.18	37.72	38.71	38.22	38.63	39.61	36.58	36.90	38.18	39.34	39.66	40.22	40.15	40.10	40.79	40.30	40.32	40.97	40.22	40.28	40.88	38.03	38.31	39.02	38.57	39.53	40.72	36.96	36.91	37.49
12	41.98	43.64	44.32	41.27	41.51	42.55	42.07	42.54	43.22	40.35	40.49	41.79	43.14	43.60	43.97	43.73	43.47	44.10	43.89	43.72	44.42	43.68	43.95	44.48	41.70	42.30	42.82	41.69	43.31	44.23	40.80	40.93	40.99
13	34.77	34.61	35.78	32.60	32.87	33.67	33.16	33.18	34.18	32.03	32.35	32.98	34.04	34.12	34.72	35.41	35.32	36.19	35.22	35.17	36.36	35.00	34.79	35.87	33.05	32.91	33.80	34.66	34.07	35.52	32.04	31.69	32.56
14	34.55	36.50	37.28	32.73	33.81	34.26	34.13	35.38	35.57	32.03	33.80	33.59	35.09	36.16	36.05	35.64	35.60	36.13	35.92	35.98	35.88	35.38	36.20	36.22	33.55	34.80	34.52	34.46	36.12	36.57	32.23	33.21	32.88
15	39.42	40.04	40.15	37.51	37.92	38.13	38.32	38.92	38.79	36.94	37.76	37.52	39.26	39.77	39.59	39.77	39.61	39.48	39.91	39.76	39.80	39.83	40.17	40.09	37.85	38.37	38.28	39.03	39.96	39.92	36.90	37.12	36.69
16	40.91	43.40	44.93	41.32	41.65	42.63	41.86	42.15	43.49	40.42	39.47	42.21	43.23	43.46	44.06	44.49	44.35	44.99	44.71	44.68	45.43	44.06	44.05	44.78	41.59	41.66	42.62	40.90	42.92	44.84	40.38	39.81	41.22
17	41.15	41.62	42.34	39.39	40.04	40.55	39.97	40.46	41.08	38.73	39.83	40.15	40.54	41.12	41.32	41.75	41.83	42.32	41.80	41.88	42.31	41.57	41.94	42.52	39.79	40.32	40.70	41.21	41.33	42.09	38.93	38.99	39.85
18	36.96	37.29	38.08	34.94	35.49	35.88	35.79	36.17	36.68	34.70	35.51	35.39	36.36	36.71	36.98	37.11	37.12	37.58	36.83	36.82	37.18	37.03	37.28	37.86	35.59	35.94	36.30	36.82	36.86	37.60	34.61	34.82	35.20
19	37.94	40.16	41.72	38.37	39.05	40.28	39.33	39.61	40.90	37.59	36.76	39.78	40.38	40.62	41.49	41.39	41.33	42.06	41.41	41.32	42.26	41.24	41.29	41.98	39.44	39.54	40.58	37.93	39.73	41.56	38.52	38.34	39.33
20	40.04	41.08	42.05	38.48	39.15	40.32	39.25	40.01	40.87	37.93	39.08	39.80	40.10	40.78	41.37	40.98	40.99	41.96	41.19	41.22	41.85	40.82	41.38	42.08	38.68	39.43	40.32	40.28	40.81	42.14	37.49	37.61	39.40
21	38.21	39.25	40.33	36.81	37.58	38.18	38.14	38.33	39.29	36.38	36.32	37.81	39.05	39.31	39.80	40.18	40.14	40.83	40.20	40.18	41.03	39.98	40.03	40.76	38.06	38.20	38.85	38.29	38.90	40.35	37.03	36.84	37.59
22	37.26	38.46	39.03	36.06	36.79	37.08	37.02	37.75	37.89	35.52	36.31	36.59	37.59	38.28	38.36	38.81	38.75	38.71	38.84	38.80	38.88	38.39	39.01	39.09	36.89	37.59	37.58	37.18	38.10	38.72	36.04	36.45	36.34
23	41.16	42.89	43.65	38.92	39.74	40.45	40.60	41.71	42.02	38.13	40.01	39.70	41.60	42.45	42.38	41.95	41.79	42.31	42.26	42.01	42.20	41.83	42.50	42.68	39.99	41.20	41.15	40.88	42.35	42.92	38.34	39.11	39.11
24	34.63	35.32	35.62	33.58	34.38	34.51	33.82	34.57	34.88	32.97	34.13	33.84	34.32	35.18	35.13	36.31	36.29	36.28	36.22	36.24	36.51	35.38	36.20	36.45	33.50	34.13	34.50	34.85	35.09	35.84	33.07	32.88	33.87
mean	38.60	39.95	40.84	37.29	37.88	38.70	38.25	38.83	39.54	36.59	37.19	38.10	39.19	39.72	40.10	39.34	39.28	39.79	39.40	39.37	39.91	39.89	40.22	40.73	37.88	38.44	38.93	38.47	39.55	40.61	36.82	37.02	37.51

Table 3: Demosaicking performance (mean CIE Lab error) on Kodak dataset (noise free) with all 11 CFAs

LAB	Bayer CFA			Kodak CFA2.0			Fuji X-Trans			Sony RGBW			Random CFA			Hirakawa 4 color			Condat 6 color			Hao4x4			Hao40			Hao50			Hao60
image	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours
1	1.750	1.561	1.325	2.247	2.145	1.895	1.857	1.881	1.632	2.476	2.572	2.075	1.612	1.622	1.480	1.723	1.769	1.575	1.700	1.743	1.482	1.788	1.852	1.703	2.118	2.147	1.921	2.130	1.956	1.666	2.603	2.686	2.502
2	1.957	1.663	1.374	2.442	2.200	1.876	2.035	1.814	1.599	2.720	2.278	2.037	1.837	1.689	1.547	1.961	2.022	1.759	1.865	1.916	1.739	2.094	2.014	1.919	2.245	2.018	1.826	2.373	2.078	1.818	3.144	2.694	2.492
3	1.125	0.870	0.829	1.472	1.271	1.094	1.269	1.001	0.945	1.607	1.210	1.161	1.127	0.923	0.885	1.043	1.051	0.976	1.015	1.011	0.964	1.260	1.045	0.989	1.511	1.209	1.160	1.426	1.093	0.997	2.066	1.728	1.551
4	1.209	1.136	1.096	1.823	1.739	1.686	1.410	1.334	1.324	1.951	1.772	1.829	1.338	1.286	1.289	1.465	1.478	1.470	1.441	1.451	1.430	1.480	1.459	1.446	1.677	1.583	1.589	1.532	1.412	1.472	1.983	1.931	2.099
5	2.406	1.939	1.784	4.178	3.490	3.167	3.061	2.550	2.351	4.527	3.537	3.367	2.687	2.269	2.218	2.853	2.800	2.619	2.742	2.692	2.548	3.019	2.621	2.422	3.500	2.953	2.739	3.076	2.562	2.227	4.663	4.012	3.526
6	1.305	1.107	0.945	1.725	1.578	1.477	1.405	1.340	1.168	1.805	1.817	1.532	1.203	1.151	1.067	1.223	1.239	1.122	1.192	1.188	1.067	1.356	1.318	1.212	1.555	1.532	1.393	1.589	1.390	1.142	1.932	1.991	1.792
7	1.190	0.975	0.913	1.778	1.653	1.384	1.364	1.189	1.094	1.916	1.613	1.452	1.202	1.073	1.000	1.308	1.314	1.228	1.270	1.260	1.202	1.384	1.268	1.193	1.545	1.356	1.232	1.545	1.312	1.153	2.152	1.890	1.694
8	2.118	1.728	1.379	2.484	2.404	1.867	2.039	2.009	1.593	2.639	2.958	1.924	1.768	1.736	1.471	1.925	1.950	1.670	1.882	1.906	1.617	1.989	1.992	1.778	2.238	2.222	1.835	2.715	2.238	1.726	2.750	2.816	2.288
9	0.926	0.776	0.697	1.245	1.152	0.989	1.006	0.906	0.810	1.297	1.196	1.016	0.931	0.850	0.777	0.990	0.996	0.921	0.968	0.975	0.878	1.068	0.987	0.910	1.125	1.011	0.920	1.183	1.017	0.895	1.439	1.334	1.131
10	0.816	0.777	0.724	1.203	1.162	1.034	0.933	0.902	0.837	1.227	1.172	1.062	0.880	0.848	0.801	1.011	0.999	0.944	0.963	0.962	0.887	0.994	0.975	0.921	1.089	1.038	0.965	1.024	0.992	0.905	1.352	1.345	1.161
11	1.607	1.382	1.180	2.184	2.016	1.750	1.752	1.640	1.428	2.294	2.211	1.817	1.520	1.446	1.321	1.611	1.619	1.451	1.565	1.568	1.403	1.708	1.670	1.548	1.916	1.850	1.634	1.947	1.729	1.494	2.502	2.457	2.121
12	0.683	0.615	0.569	0.865	0.873	0.775	0.737	0.716	0.661	0.939	0.930	0.827	0.672	0.651	0.619	0.727	0.746	0.686	0.715	0.727	0.666	0.753	0.742	0.698	0.846	0.814	0.757	0.843	0.758	0.691	1.023	1.045	0.970
13	2.556	2.542	2.234	4.001	3.687	3.430	3.277	3.156	2.868	4.187	3.881	3.666	2.844	2.752	2.583	2.896	2.922	2.683	2.953	2.959	2.648	3.191	3.141	2.847	3.584	3.541	3.227	3.173	3.233	2.876	4.711	4.626	4.214
14	2.703	1.832	1.607	3.805	2.785	2.569	2.966	2.147	1.985	4.082	2.850	2.729	2.560	1.919	1.858	2.314	2.327	2.097	2.194	2.190	2.102	2.838	2.256	2.155	3.345	2.501	2.396	3.380	2.372	2.073	4.847	3.544	3.186
15	1.446	1.350	1.290	2.127	2.035	1.903	1.659	1.554	1.521	2.226	2.019	1.998	1.592	1.507	1.486	1.733	1.753	1.704	1.692	1.712	1.659	1.787	1.737	1.692	1.936	1.816	1.777	1.804	1.671	1.651	2.336	2.238	2.216
16	1.092	0.907	0.789	1.300	1.249	1.150	1.106	1.072	0.946	1.372	1.465	1.186	0.960	0.939	0.881	0.976	0.993	0.926	0.950	0.956	0.884	1.070	1.065	0.997	1.270	1.272	1.153	1.341	1.132	0.949	1.554	1.677	1.425
17	1.348	1.297	1.203	1.897	1.792	1.672	1.588	1.510	1.415	1.960	1.811	1.705	1.482	1.414	1.364	1.581	1.569	1.491	1.557	1.550	1.468	1.619	1.573	1.489	1.721	1.648	1.559	1.662	1.635	1.536	2.108	2.056	1.880
18	2.389	2.136	2.033	3.684	3.261	3.165	2.912	2.624	2.560	3.888	3.318	3.349	2.578	2.363	2.355	2.786	2.787	2.696	2.747	2.745	2.731	2.961	2.750	2.630	3.210	2.906	2.839	3.100	2.846	2.670	4.294	3.826	3.643
19	1.372	1.217	1.045	1.812	1.663	1.507	1.494	1.432	1.274	1.939	1.892	1.588	1.312	1.271	1.174	1.399	1.406	1.297	1.366	1.375	1.247	1.445	1.428	1.333	1.591	1.563	1.413	1.679	1.546	1.320	2.019	2.017	1.798
20	1.093	0.976	0.883	1.519	1.383	1.203	1.266	1.144	1.035	1.587	1.387	1.254	1.131	1.027	0.952	1.200	1.193	1.077	1.148	1.142	1.047	1.268	1.161	1.060	1.431	1.295	1.158	1.314	1.214	1.049	1.899	1.802	1.447
21	1.374	1.284	1.126	2.021	1.857	1.728	1.547	1.516	1.362	2.067	1.996	1.776	1.375	1.349	1.273	1.458	1.466	1.341	1.441	1.444	1.316	1.519	1.517	1.400	1.703	1.694	1.566	1.654	1.601	1.388	2.147	2.214	1.948
22	1.439	1.331	1.299	2.082	1.990	1.906	1.625	1.551	1.553	2.185	2.039	2.006	1.486	1.420	1.426	1.622	1.638	1.646	1.608	1.619	1.621	1.670	1.627	1.603	1.826	1.734	1.732	1.829	1.719	1.614	2.286	2.221	2.186
23	0.963	0.871	0.843	1.356	1.344	1.207	1.070	0.993	0.981	1.449	1.280	1.281	0.971	0.918	0.923	1.099	1.121	1.066	1.056	1.080	1.079	1.106	1.065	1.045	1.197	1.098	1.095	1.196	1.103	1.042	1.610	1.539	1.443
24	1.487	1.400	1.289	2.252	2.037	1.907	1.760	1.675	1.554	2.354	2.132	2.031	1.582	1.495	1.444	1.651	1.666	1.585	1.630	1.636	1.526	1.735	1.682	1.575	2.012	1.921	1.765	1.813	1.762	1.544	2.473	2.508	2.148
mean	1.515	1.320	1.186	2.146	1.949	1.764	1.714	1.569	1.437	2.279	2.056	1.861	1.527	1.413	1.341	1.606	1.618	1.501	1.569	1.575	1.467	1.713	1.623	1.524	1.925	1.780	1.652	1.889	1.682	1.496	2.496	2.342	2.119

Table 4: Demosaicking performance (CMSE) on Kodak dataset (noise std=5) with all 11 CFAs

CMSE	Bayer CFA			Kodak CFA2.0			Fuji X-Trans			Sony RGBW			Random CFA			Hirakawa 4 color			Condat 6 color			Hao4x4			Hao40			Hao50			Hao60
Image	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours
1	34.69	31.97	30.69	39.28	40.88	36.80	34.31	34.69	33.18	42.63	46.99	38.90	31.02	31.04	30.28	33.05	33.34	33.19	32.37	32.59	30.93	32.44	32.96	32.13	36.05	36.26	34.91	39.72	37.78	36.13	41.07	42.46	42.54
2	29.94	28.81	27.80	35.36	37.19	33.99	31.31	30.07	29.84	36.85	36.57	34.16	29.38	28.59	28.21	32.32	32.65	32.61	31.33	31.61	30.80	31.93	31.30	31.57	32.61	31.25	31.41	34.89	33.47	33.58	36.05	34.61	37.03
3	27.26	25.59	26.37	33.17	34.35	32.49	29.45	27.11	28.63	34.57	32.35	32.40	27.84	26.06	26.91	30.22	29.93	30.78	29.07	28.65	29.58	30.57	28.99	29.58	32.46	29.43	31.10	32.23	30.45	31.86	38.02	35.93	37.39
4	28.11	27.23	28.15	35.55	36.77	35.82	30.48	28.91	30.64	36.90	35.56	36.31	28.55	27.42	28.63	31.78	31.62	33.09	30.71	30.42	31.31	31.02	30.11	31.07	32.33	30.35	32.37	33.63	32.11	34.40	35.79	34.53	39.36
5	34.96	31.55	31.90	53.08	48.95	47.13	41.77	37.08	37.99	57.90	47.80	49.34	37.37	33.65	35.28	39.22	38.28	39.63	37.77	36.84	37.69	38.29	35.71	35.87	45.04	39.67	41.05	40.57	37.54	38.09	53.27	48.98	50.06
6	32.13	28.58	27.94	36.72	36.95	34.11	32.30	30.52	29.81	37.57	40.33	33.80	29.48	27.85	27.94	30.87	30.69	30.94	30.15	29.80	29.42	31.60	30.27	30.07	32.84	31.58	31.39	36.69	34.03	33.22	37.02	37.34	37.00
7	26.62	25.42	26.35	33.92	35.03	33.14	28.72	27.07	28.41	34.69	33.35	32.79	27.13	25.96	26.79	30.49	30.26	31.22	29.40	29.02	29.91	30.14	29.15	29.70	30.57	28.53	29.94	32.03	30.81	32.26	35.46	33.83	35.24
8	54.25	38.85	34.24	48.88	48.19	39.09	42.21	40.79	35.54	54.07	68.26	39.79	36.47	35.01	32.25	37.29	37.32	35.79	36.22	36.22	33.77	37.40	36.47	34.86	42.49	41.17	37.08	59.12	45.13	39.14	49.25	49.40	44.48
9	27.14	25.81	25.90	32.51	34.07	31.48	28.22	27.03	27.70	32.80	33.35	30.60	26.75	25.87	26.22	30.26	30.04	30.64	29.13	28.87	28.79	29.70	28.97	29.31	29.51	28.09	28.88	32.07	31.03	31.59	33.97	33.28	33.04
10	26.27	25.66	26.31	32.65	34.09	31.64	28.12	26.80	27.78	32.05	32.39	30.51	26.80	25.68	26.30	30.32	29.91	30.68	28.97	28.64	28.74	29.51	28.61	29.13	31.05	28.71	30.02	31.20	30.71	31.70	35.25	33.75	33.57
11	30.76	29.07	28.35	37.81	38.65	35.22	32.57	31.21	30.88	38.89	39.38	35.17	29.90	28.92	28.87	32.29	32.12	32.34	31.27	30.97	30.78	31.82	31.23	31.22	33.86	32.65	32.56	35.58	34.20	34.09	38.69	38.66	38.54
12	25.92	24.82	25.39	30.28	32.22	30.19	26.81	25.73	26.85	30.53	31.78	29.50	25.49	24.57	25.24	28.67	28.55	29.40	27.70	27.48	27.81	28.41	27.60	28.17	28.02	26.70	27.83	30.92	29.79	30.89	30.97	30.54	32.17
13	43.49	44.44	40.06	61.12	61.11	54.53	54.15	53.27	48.34	65.31	63.80	58.70	47.96	46.82	44.40	44.46	44.62	42.44	44.57	44.54	40.32	46.20	46.49	42.53	55.68	56.04	51.36	48.69	52.03	46.58	65.82	68.80	62.88
14	44.59	37.03	35.69	60.20	54.78	52.55	47.88	41.27	42.53	65.25	53.19	55.14	42.54	37.77	39.41	43.70	43.56	43.56	41.78	41.26	42.89	44.55	40.85	42.00	52.27	44.58	48.20	49.95	42.96	43.46	64.35	56.33	60.76
15	29.24	27.85	28.59	36.91	37.33	35.93	32.30	29.88	31.58	37.72	36.17	36.10	30.04	28.03	29.31	32.06	32.01	33.47	31.06	30.93	31.52	32.37	30.54	31.46	34.29	31.66	33.33	34.65	32.56	34.32	38.73	37.14	39.77
16	27.09	25.32	25.47	30.23	32.35	30.07	26.96	26.25	26.73	30.53	33.70	29.37	25.42	24.85	25.23	28.50	28.32	29.16	27.51	27.25	27.52	28.21	27.81	28.22	28.13	27.54	28.03	31.87	30.45	30.96	31.55	32.31	32.07
17	26.82	26.47	26.76	32.95	34.17	32.12	29.29	28.00	28.73	33.29	32.71	31.27	28.08	26.87	27.53	30.40	30.04	30.66	29.55	29.15	29.19	30.22	29.32	29.55	30.43	29.00	29.89	31.41	31.30	32.12	33.97	33.66	33.60
18	34.95	34.22	33.10	46.33	46.07	43.25	39.96	37.84	37.55	46.74	44.34	44.19	37.37	35.67	35.67	38.60	38.31	38.17	38.59	38.28	37.68	38.53	37.19	36.37	41.61	39.46	39.58	40.12	40.01	39.50	48.11	46.63	46.35
19	32.26	28.41	27.58	34.91	35.89	32.61	30.38	29.33	29.04	35.96	39.84	32.09	28.30	27.50	27.36	30.82	30.60	30.87	29.98	29.74	29.35	30.57	30.03	30.06	31.00	30.19	30.00	36.99	33.72	33.08	34.74	35.00	34.41
20	28.30	23.15	23.31	34.59	30.74	28.25	30.47	24.72	25.06	35.07	29.52	27.95	28.67	23.35	23.74	26.69	26.43	26.58	25.64	25.35	25.30	31.05	25.29	25.37	32.36	26.18	26.41	32.64	27.63	27.75	37.06	32.04	29.84
21	31.65	29.83	29.25	38.91	39.06	36.55	32.70	31.70	31.33	39.58	41.30	36.23	30.41	29.42	29.56	32.28	32.08	32.22	31.37	31.11	30.78	32.12	31.51	31.19	33.75	32.75	32.76	36.20	35.20	34.69	38.56	38.93	38.44
22	34.06	31.36	31.26	41.51	41.31	39.53	35.67	33.09	34.18	42.85	41.42	39.94	33.62	31.47	32.15	34.62	34.44	35.82	33.66	33.41	33.74	35.07	33.30	33.92	36.93	34.27	35.69	39.03	36.86	37.20	41.80	40.18	41.88
23	26.82	25.33	25.68	33.75	34.48	33.09	28.42	26.52	28.16	34.57	32.44	32.97	26.86	25.49	26.79	30.05	29.90	30.87	28.91	28.76	29.50	29.88	28.61	29.50	30.12	27.76	29.75	31.83	30.35	31.72	35.10	33.33	35.28
24	44.18	40.61	40.45	53.90	50.80	48.66	49.71	44.41	44.18	57.43	50.64	51.66	46.30	40.99	42.00	40.64	40.42	41.42	40.09	39.64	39.21	44.46	40.09	39.96	52.62	47.44	46.78	47.82	46.34	44.90	57.55	58.46	52.89
mean	32.56	29.89	29.44	39.77	39.81	37.01	34.34	32.22	32.28	41.41	40.72	37.45	31.74	29.95	30.25	33.32	33.14	33.56	32.37	32.10	31.94	33.59	32.18	32.20	36.08	33.80	34.18	37.49	35.27	35.13	41.34	40.26	40.36

Table 5: Demosaicking performance (CPSNR) on Kodak dataset (noise std=5) with all 11 CFAs

CPSNR	Bayer CFA			Kodak CFA2.0			Fuji X-Trans			Sony RGBW			Random CFA			Hirakawa 4 color			Condat 6 color			Hao4x4			Hao40			Hao50			Hao60
image	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours
1	32.73	33.08	33.26	32.19	32.02	32.47	32.78	32.73	32.92	31.83	31.41	32.23	33.21	33.21	33.32	32.94	32.90	32.92	33.03	33.00	33.23	33.02	32.95	33.06	32.56	32.54	32.70	32.14	32.36	32.55	32.00	31.85	31.84
2	33.37	33.54	33.69	32.65	32.43	32.82	33.17	33.35	33.38	32.47	32.50	32.80	33.45	33.57	33.63	33.04	32.99	33.00	33.17	33.13	33.25	33.09	33.18	33.14	33.00	33.18	33.16	32.70	32.88	32.87	32.56	32.74	32.44
3	33.77	34.05	33.92	32.92	32.77	33.01	33.44	33.80	33.56	32.74	33.03	33.03	33.68	33.97	33.83	33.33	33.37	33.25	33.50	33.56	33.42	33.28	33.51	33.42	33.02	33.44	33.20	33.05	33.30	33.10	32.33	32.58	32.40
4	33.64	33.78	33.64	32.62	32.48	32.59	33.29	33.52	33.27	32.46	32.62	32.53	33.57	33.75	33.56	33.11	33.13	32.93	33.26	33.30	33.17	33.21	33.34	33.21	33.04	33.31	33.03	32.86	33.07	32.77	32.59	32.75	32.18
5	32.70	33.14	33.09	30.88	31.23	31.40	31.92	32.44	32.33	30.50	31.34	31.20	32.41	32.86	32.66	32.20	32.30	32.15	32.36	32.47	32.37	32.30	32.60	32.58	31.59	32.15	32.00	32.05	32.39	32.32	30.87	31.23	31.14
6	33.06	33.57	33.67	32.48	32.45	32.80	33.04	33.29	33.39	32.38	32.07	32.84	33.44	33.68	33.67	33.24	33.26	33.23	33.34	33.39	33.44	33.13	33.32	33.35	32.97	33.14	33.16	32.48	32.81	32.92	32.45	32.41	32.45
7	33.88	34.08	33.92	32.83	32.69	32.93	33.55	33.81	33.60	32.73	32.90	32.97	33.80	33.99	33.85	33.29	33.32	33.19	33.45	33.50	33.37	33.34	33.48	33.40	33.28	33.58	33.37	33.08	33.24	33.04	32.63	32.84	32.66
8	30.79	32.24	32.79	31.24	31.30	32.21	31.88	32.03	32.62	30.80	29.79	32.13	32.51	32.69	33.05	32.42	32.41	32.59	32.54	32.54	32.85	32.40	32.51	32.71	31.85	31.98	32.44	30.41	31.59	32.20	31.21	31.19	31.65
9	33.80	34.01	34.00	33.01	32.81	33.15	33.63	33.81	33.71	32.97	32.90	33.27	33.86	34.00	33.94	33.32	33.35	33.27	33.49	33.53	33.54	33.40	33.51	33.46	33.43	33.64	33.53	33.07	33.21	33.14	32.82	32.91	32.94
10	33.94	34.04	33.93	32.99	32.80	33.13	33.64	33.85	33.69	33.07	33.03	33.29	33.85	34.03	33.93	33.31	33.37	33.26	33.51	33.56	33.55	33.43	33.57	33.49	33.21	33.55	33.36	33.19	33.26	33.12	32.66	32.85	32.87
11	33.25	33.50	33.61	32.35	32.26	32.66	33.00	33.19	33.23	32.23	32.18	32.67	33.37	33.52	33.53	33.04	33.06	33.03	33.18	33.22	33.25	33.10	33.18	33.19	32.83	32.99	33.00	32.62	32.79	32.80	32.25	32.26	32.27
12	33.99	34.18	34.08	33.32	33.05	33.33	33.85	34.03	33.84	33.28	33.11	33.43	34.07	34.23	34.11	33.56	33.58	33.45	33.71	33.74	33.69	33.60	33.72	33.63	33.66	33.87	33.69	33.23	33.39	33.23	33.22	33.28	33.06
13	31.75	31.65	32.10	30.27	30.27	30.76	30.79	30.87	31.29	29.98	30.08	30.44	31.32	31.43	31.66	31.65	31.64	31.85	31.64	31.64	32.08	31.48	31.46	31.84	30.67	30.65	31.02	31.26	30.97	31.45	29.95	29.75	30.15
14	31.64	32.45	32.61	30.33	30.74	30.92	31.33	31.97	31.84	29.98	30.87	30.72	31.84	32.36	32.18	31.73	31.74	31.74	31.92	31.98	31.81	31.64	32.02	31.90	30.95	31.64	31.30	31.15	31.80	31.75	30.05	30.62	30.29
15	33.47	33.68	33.57	32.46	32.41	32.58	33.04	33.38	33.14	32.37	32.55	32.56	33.35	33.66	33.46	33.07	33.08	32.88	33.21	33.23	33.15	33.03	33.28	33.15	32.78	33.13	32.90	32.73	33.00	32.78	32.25	32.43	32.14
16	33.80	34.10	34.07	33.33	33.03	33.35	33.82	33.94	33.86	33.28	32.85	33.45	34.08	34.18	34.11	33.58	33.61	33.48	33.74	33.78	33.73	33.63	33.69	33.62	33.64	33.73	33.65	33.10	33.30	33.22	33.14	33.04	33.07
17	33.85	33.90	33.86	32.95	32.79	33.06	33.46	33.66	33.55	32.91	32.98	33.18	33.65	33.84	33.73	33.30	33.35	33.27	33.43	33.48	33.48	33.33	33.46	33.43	33.30	33.51	33.38	33.16	33.18	33.06	32.82	32.86	32.87
18	32.70	32.79	32.93	31.47	31.50	31.77	32.11	32.35	32.39	31.43	31.66	31.68	32.41	32.61	32.61	32.27	32.30	32.31	32.27	32.30	32.37	32.27	32.43	32.52	31.94	32.17	32.16	32.10	32.11	32.16	31.31	31.44	31.47
19	33.04	33.60	33.72	32.70	32.58	33.00	33.30	33.46	33.50	32.57	32.13	33.07	33.61	33.74	33.76	33.24	33.27	33.24	33.36	33.40	33.46	33.28	33.35	33.35	33.22	33.33	33.36	32.45	32.85	32.94	32.72	32.69	32.76
20	33.61	34.49	34.46	32.74	33.25	33.62	33.29	34.20	34.14	32.68	33.43	33.67	33.56	34.45	34.38	33.87	33.91	33.89	34.04	34.09	34.10	33.21	34.10	34.09	33.03	33.95	33.91	32.99	33.72	33.70	32.44	33.07	33.38
21	33.13	33.38	33.47	32.23	32.21	32.50	32.99	33.12	33.17	32.16	31.97	32.54	33.30	33.44	33.42	33.04	33.07	33.05	33.17	33.20	33.25	33.06	33.15	33.19	32.85	32.98	32.98	32.54	32.67	32.73	32.27	32.23	32.28
22	32.81	33.17	33.18	31.95	31.97	32.16	32.61	32.93	32.79	31.81	31.96	32.12	32.86	33.15	33.06	32.74	32.76	32.59	32.86	32.89	32.85	32.68	32.91	32.83	32.46	32.78	32.61	32.22	32.46	32.42	31.92	32.09	31.91
23	33.85	34.09	34.03	32.85	32.76	32.93	33.59	33.90	33.63	32.74	33.02	32.95	33.84	34.07	33.85	33.35	33.37	33.24	33.52	33.54	33.43	33.38	33.57	33.43	33.34	33.70	33.40	33.10	33.31	33.12	32.68	32.90	32.66
24	31.68	32.04	32.06	30.82	31.07	31.26	31.17	31.66	31.68	30.54	31.09	31.00	31.48	32.00	31.90	32.04	32.07	31.96	32.10	32.15	32.20	31.65	32.10	32.11	30.92	31.37	31.43	31.34	31.47	31.61	30.53	30.46	30.90
mean	33.09	33.44	33.49	32.23	32.20	32.52	32.86	33.14	33.11	32.08	32.14	32.49	33.19	33.43	33.38	32.90	32.93	32.87	33.03	33.07	33.09	32.91	33.10	33.09	32.65	32.93	32.86	32.46	32.71	32.71	32.07	32.19	32.16

Table 6: Demosaicking performance (mean CIE Lab error) on Kodak dataset (noise std=5) with all 11 CFAs

LAB	Bayer CFA			Kodak CFA2.0			Fuji X-Trans			Sony RGBW			Random CFA			Hirakawa 4 color			Condat 6 color			Hao4x4			Hao40			Hao50			Hao60
image	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours
1	2.713	2.670	2.443	3.197	3.438	2.983	2.775	2.845	2.600	3.281	3.631	3.003	2.601	2.652	2.492	2.985	3.021	2.860	2.957	2.997	2.732	2.959	3.056	2.923	2.990	3.093	2.872	3.414	3.444	3.317	3.516	3.692	3.576
2	3.921	3.765	3.502	4.472	4.671	4.066	3.989	3.800	3.581	4.529	4.518	3.948	3.850	3.717	3.552	4.303	4.324	4.182	4.199	4.230	4.003	4.340	4.231	4.170	4.141	3.982	3.800	5.101	4.944	4.927	5.117	4.763	4.717
3	2.637	2.503	2.378	3.031	3.229	2.775	2.727	2.523	2.405	3.005	3.008	2.616	2.631	2.476	2.378	2.887	2.885	2.823	2.846	2.852	2.735	3.020	2.854	2.774	2.921	2.728	2.623	3.412	3.250	3.254	3.582	3.336	3.207
4	2.584	2.653	2.520	3.141	3.500	3.112	2.701	2.710	2.608	3.127	3.350	3.027	2.679	2.693	2.605	3.135	3.134	3.102	3.098	3.107	2.977	3.041	3.073	3.018	2.906	2.942	2.858	3.340	3.437	3.530	3.345	3.434	3.515
5	3.904	3.598	3.425	5.481	5.188	4.724	4.398	3.963	3.784	5.657	5.057	4.702	4.115	3.759	3.683	4.537	4.481	4.385	4.424	4.389	4.228	4.702	4.346	4.191	4.775	4.337	4.169	5.009	4.691	4.615	5.958	5.386	5.093
6	2.185	2.147	1.979	2.580	2.777	2.429	2.236	2.246	2.048	2.560	2.840	2.352	2.098	2.108	1.990	2.399	2.427	2.310	2.367	2.387	2.221	2.439	2.465	2.352	2.347	2.421	2.257	2.747	2.765	2.658	2.786	2.927	2.752
7	2.466	2.390	2.298	3.001	3.216	2.771	2.575	2.464	2.347	3.003	3.049	2.661	2.469	2.385	2.293	2.839	2.842	2.791	2.778	2.784	2.682	2.855	2.777	2.704	2.705	2.616	2.492	3.201	3.147	3.156	3.375	3.220	3.097
8	2.926	2.689	2.380	3.260	3.490	2.832	2.808	2.840	2.466	3.327	3.857	2.777	2.608	2.629	2.392	2.991	3.003	2.789	2.961	2.982	2.715	2.992	3.044	2.872	2.987	3.061	2.728	3.725	3.470	3.167	3.519	3.670	3.255
9	1.863	1.822	1.684	2.161	2.393	2.004	1.889	1.852	1.709	2.131	2.313	1.898	1.847	1.821	1.702	2.145	2.153	2.069	2.123	2.138	1.986	2.164	2.137	2.050	1.986	1.967	1.841	2.337	2.335	2.277	2.373	2.352	2.170
10	1.826	1.899	1.780	2.172	2.508	2.090	1.877	1.915	1.784	2.108	2.380	1.972	1.862	1.893	1.781	2.241	2.231	2.165	2.207	2.213	2.069	2.175	2.217	2.144	1.999	2.054	1.932	2.282	2.417	2.380	2.321	2.424	2.239
11	3.220	3.138	2.893	3.824	4.096	3.467	3.318	3.236	2.977	3.766	4.024	3.309	3.157	3.101	2.914	3.592	3.598	3.449	3.528	3.541	3.312	3.643	3.599	3.476	3.441	3.436	3.193	4.133	4.053	3.967	4.167	4.167	3.884
12	1.542	1.590	1.488	1.744	2.065	1.740	1.560	1.605	1.503	1.708	1.979	1.641	1.531	1.567	1.485	1.840	1.853	1.792	1.816	1.834	1.718	1.787	1.832	1.770	1.646	1.708	1.613	1.973	2.066	2.046	1.889	2.006	1.937
13	3.444	3.481	3.166	4.760	4.737	4.302	4.034	3.943	3.645	4.848	4.769	4.404	3.675	3.602	3.418	3.970	3.994	3.763	3.981	3.998	3.657	4.198	4.156	3.894	4.313	4.311	4.011	4.358	4.489	4.268	5.439	5.379	5.041
14	4.044	3.322	3.056	5.047	4.490	4.006	4.216	3.480	3.279	5.185	4.347	3.954	3.896	3.308	3.190	3.949	3.967	3.788	3.846	3.867	3.707	4.436	3.895	3.796	4.516	3.787	3.655	5.164	4.334	4.193	6.046	4.833	4.565
15	3.330	3.277	3.111	3.984	4.285	3.768	3.457	3.343	3.210	3.914	4.068	3.606	3.443	3.329	3.203	3.859	3.858	3.812	3.823	3.834	3.659	3.949	3.816	3.739	3.704	3.609	3.474	4.215	4.203	4.234	4.252	4.183	4.111
16	2.214	2.221	2.054	2.452	2.845	2.397	2.183	2.247	2.057	2.395	2.840	2.265	2.113	2.164	2.036	2.486	2.518	2.404	2.456	2.490	2.325	2.463	2.539	2.437	2.311	2.434	2.258	2.789	2.871	2.805	2.668	2.891	2.671
17	3.497	3.587	3.339	4.068	4.631	3.866	3.610	3.619	3.359	3.926	4.377	3.613	3.587	3.581	3.368	4.205	4.197	4.047	4.150	4.163	3.866	4.144	4.144	3.984	3.729	3.813	3.571	4.454	4.623	4.540	4.362	4.477	4.238
18	3.972	3.835	3.620	5.166	5.152	4.678	4.356	4.101	3.942	5.204	5.000	4.648	4.088	3.898	3.796	4.582	4.572	4.480	4.528	4.531	4.368	4.730	4.523	4.369	4.604	4.367	4.246	5.123	4.969	4.889	5.784	5.348	5.185
19	2.418	2.369	2.165	2.818	3.048	2.603	2.473	2.463	2.263	2.842	3.092	2.529	2.342	2.341	2.199	2.716	2.718	2.599	2.673	2.687	2.487	2.700	2.719	2.604	2.545	2.595	2.404	3.028	3.052	2.939	3.038	3.118	2.909
20	2.084	2.007	1.891	2.504	2.603	2.243	2.189	2.072	1.945	2.470	2.495	2.156	2.100	1.993	1.902	2.364	2.352	2.261	2.296	2.297	2.167	2.434	2.295	2.199	2.327	2.222	2.082	2.633	2.580	2.513	2.857	2.732	2.476
21	2.329	2.349	2.158	2.924	3.119	2.745	2.430	2.464	2.265	2.881	3.109	2.656	2.313	2.337	2.221	2.658	2.670	2.545	2.643	2.660	2.478	2.647	2.695	2.567	2.564	2.640	2.475	2.883	2.992	2.885	3.073	3.210	2.988
22	2.419	2.441	2.326	3.002	3.256	2.930	2.542	2.545	2.456	2.999	3.173	2.886	2.444	2.445	2.368	2.844	2.844	2.842	2.808	2.819	2.714	2.811	2.835	2.769	2.693	2.716	2.636	3.093	3.176	3.133	3.208	3.261	3.202
23	2.316	2.340	2.228	2.711	3.082	2.685	2.372	2.365	2.276	2.664	2.882	2.567	2.320	2.315	2.244	2.743	2.747	2.707	2.686	2.705	2.584	2.678	2.685	2.628	2.469	2.480	2.413	2.969	3.063	3.053	2.962	3.028	2.944
24	2.549	2.587	2.422	3.245	3.435	3.019	2.750	2.736	2.553	3.232	3.349	2.987	2.625	2.598	2.492	2.979	2.980	2.902	2.943	2.950	2.775	2.997	2.996	2.878	2.963	2.982	2.778	3.192	3.332	3.223	3.474	3.627	3.288
mean	2.767	2.695	2.513	3.364	3.552	3.093	2.894	2.807	2.628	3.365	3.479	3.007	2.766	2.696	2.571	3.135	3.140	3.036	3.089	3.102	2.924	3.179	3.122	3.013	3.066	3.012	2.849	3.524	3.488	3.415	3.713	3.644	3.461

Table 7: Demosaicking performance (CMSE) on IMAX dataset (noise free) with all 11 CFAs

CMSE	Bayer CFA			Kodak CFA2.0			Fuji X-Trans			Sony RGBW			Random CFA			Hirakawa 4 color			Condat 6 color			Hao4x4			Hao40			Hao50			Hao60
Image	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours
1	95.60	86.12	79.40	116.6	109.8	90.76	102.8	95.26	82.20	120.1	111.3	93.25	99.56	84.07	77.47	99.7	97.5	97.65	85.68	85.55	74.05	102.7	93.98	94.03	99.68	91.67	76.00	100.1	87.94	74.54	124.2	112.5	88.15
2	26.13	22.53	20.43	29.13	27.16	22.32	27.20	25.33	21.72	31.47	28.39	24.21	28.03	22.26	19.71	24.59	24.76	22.61	22.20	22.02	19.35	23.87	22.47	21.91	26.89	24.94	20.81	24.82	21.03	18.08	31.87	29.56	24.55
3	46.33	39.94	34.46	66.66	58.73	50.86	57.86	49.08	37.93	77.34	63.55	55.00	53.28	43.26	34.39	39.80	43.30	32.11	39.02	38.52	30.30	42.37	40.93	36.67	55.14	47.52	37.61	45.82	39.68	29.93	69.59	59.75	47.45
4	23.28	14.70	12.69	29.73	22.06	18.13	28.32	19.16	12.40	32.04	23.34	20.07	32.86	14.94	11.69	15.75	16.13	13.23	14.20	13.24	9.66	17.31	14.32	12.56	21.54	16.17	11.22	20.80	13.06	8.73	26.78	18.90	12.98
5	30.53	26.98	23.88	39.98	38.05	32.10	32.47	29.88	26.16	40.26	37.35	32.20	35.56	25.49	25.48	35.27	33.59	33.16	28.47	28.02	23.14	32.94	31.52	32.10	31.04	28.63	23.77	34.32	30.46	27.36	40.38	35.53	30.98
6	12.56	9.93	10.03	17.19	16.45	16.00	13.76	12.16	12.60	17.02	15.73	17.21	14.68	9.32	11.77	16.99	14.78	18.72	12.40	11.87	11.95	15.75	13.42	16.76	13.26	11.55	11.87	14.45	11.95	9.27	21.38	18.37	15.31
7	22.15	22.88	19.44	30.87	31.76	28.93	23.17	24.48	20.60	36.24	36.57	28.73	26.46	23.38	17.50	19.99	25.00	15.94	20.08	20.66	16.33	20.32	23.66	19.38	23.80	25.80	19.58	25.64	27.35	23.28	29.04	30.93	25.02
8	14.74	12.63	11.18	19.82	17.39	18.45	16.50	14.93	12.82	23.24	19.22	20.47	15.64	13.16	11.50	11.71	13.10	9.43	10.44	10.62	9.19	12.36	12.13	12.10	16.25	14.71	13.26	14.26	12.64	9.68	20.29	18.26	16.53
9	15.83	12.30	10.82	20.92	17.99	15.66	18.07	14.90	12.82	22.48	18.61	17.34	20.21	12.29	12.25	16.73	15.27	16.96	13.74	12.95	11.35	16.12	13.87	14.71	16.82	13.88	11.60	14.80	11.68	10.36	19.95	16.25	15.17
10	10.15	8.57	8.44	13.06	11.63	10.78	11.90	10.26	9.34	13.93	12.02	11.92	13.54	8.53	8.58	11.14	10.39	11.95	9.23	8.80	8.12	10.55	9.40	10.44	11.13	9.52	8.37	10.05	8.35	7.78	13.18	11.19	10.56
11	8.53	7.60	7.62	12.48	11.69	10.42	9.82	8.91	8.56	12.83	11.87	10.72	11.44	7.97	7.91	9.58	9.16	10.21	8.19	7.91	7.56	9.66	8.74	8.85	9.51	8.57	7.57	8.71	7.83	6.86	12.47	11.22	9.35
12	12.75	9.44	8.81	16.77	14.42	11.51	14.17	11.53	9.21	18.93	14.80	12.32	18.74	11.19	9.54	13.18	13.17	10.97	11.94	11.74	10.36	11.77	10.66	10.62	14.95	12.38	9.91	12.91	9.37	8.38	16.53	13.27	11.97
13	7.06	5.79	5.25	7.69	7.26	5.71	7.01	6.50	5.65	8.01	7.45	5.98	12.19	5.49	5.18	6.52	6.32	5.91	5.54	5.47	4.66	6.33	6.02	5.79	6.52	6.09	4.91	6.09	5.43	4.64	7.62	7.10	5.40
14	10.65	9.59	8.39	11.26	10.61	9.05	11.32	10.53	9.18	12.67	11.08	10.03	14.13	9.23	8.91	9.86	9.69	8.93	9.16	9.03	8.17	10.28	9.63	9.42	10.78	9.82	8.85	9.73	9.12	7.64	13.44	12.37	10.69
15	9.27	8.57	7.84	12.10	11.47	10.35	10.30	9.64	8.70	12.45	11.57	10.83	12.58	8.50	8.37	9.40	9.29	9.02	8.54	8.39	7.73	9.28	8.85	9.06	9.76	9.07	8.02	8.68	8.43	8.06	11.80	10.72	9.67
16	35.33	28.77	29.45	52.26	45.63	41.74	39.03	32.17	31.37	53.66	45.16	44.72	42.72	31.97	34.54	44.44	39.85	45.19	35.89	33.94	31.26	46.33	37.85	38.35	43.17	35.74	34.28	39.37	32.83	25.34	61.01	50.21	39.24
17	31.88	27.19	30.02	46.35	42.32	44.30	40.02	35.32	37.56	45.73	41.35	48.48	35.96	27.77	34.32	47.88	41.49	60.86	35.55	33.15	34.27	42.81	36.24	45.50	37.81	32.16	34.54	36.01	29.88	28.07	49.41	41.92	47.64
18	29.51	22.48	20.29	29.98	27.36	22.36	26.48	24.08	19.54	32.98	30.23	23.42	29.73	22.64	18.38	24.09	24.97	22.60	21.08	21.17	18.51	24.15	23.44	20.68	25.99	24.74	19.34	28.28	22.72	18.76	33.49	31.64	24.38
mean	24.57	20.89	19.36	31.82	28.99	25.52	27.23	24.12	21.02	33.96	29.98	27.05	28.74	21.19	19.86	25.37	24.87	24.75	21.74	21.28	18.66	25.27	23.17	23.27	26.34	23.50	20.08	25.27	21.65	18.15	33.47	29.43	24.72

Table 8: Demosaicking performance (CPSNR) on IMAX dataset (noise free) with all 11 CFAs

CPSNR	Bayer CFA			Kodak CFA2.0			Fuji X-Trans			Sony RGBW			Random CFA			Hirakawa 4 color			Condat 6 color			Hao4x4			Hao40			Hao50			Hao60
image	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours
1	28.33	28.78	29.13	27.46	27.73	28.55	28.01	28.34	28.98	27.34	27.67	28.43	28.15	28.88	29.24	28.1	28.2	28.23	28.8	28.8	29.44	28.02	28.40	28.40	28.14	28.51	29.32	28.13	28.69	29.41	27.19	27.62	28.68
2	33.96	34.60	35.03	33.49	33.79	34.64	33.79	34.09	34.76	33.15	33.60	34.29	33.65	34.66	35.18	34.22	34.19	34.59	34.67	34.70	35.26	34.35	34.62	34.72	33.84	34.16	34.95	34.18	34.90	35.56	33.10	33.42	34.23
3	31.47	32.12	32.76	29.89	30.44	31.07	30.51	31.22	32.34	29.25	30.10	30.73	30.86	31.77	32.77	32.13	31.77	33.06	32.22	32.27	33.32	31.86	32.01	32.49	30.72	31.36	32.38	31.52	32.15	33.37	29.71	30.37	31.37
4	34.46	36.46	37.10	33.40	34.69	35.55	33.61	35.31	37.20	33.07	34.45	35.11	32.96	36.39	37.45	36.16	36.05	36.92	36.61	36.91	38.28	35.75	36.57	37.14	34.80	36.04	37.63	34.95	36.97	38.72	33.85	35.37	37.00
5	33.28	33.82	34.35	32.11	32.33	33.07	33.02	33.38	33.95	32.08	32.41	33.05	32.62	34.07	34.07	32.66	32.87	32.93	33.59	33.66	34.49	32.95	33.15	33.07	33.21	33.56	34.37	32.78	33.29	33.76	32.07	32.63	33.22
6	37.14	38.16	38.12	35.78	35.97	36.09	36.74	37.28	37.13	35.82	36.16	35.77	36.46	38.43	37.42	35.83	36.43	35.41	37.20	37.39	37.36	36.16	36.85	35.89	36.91	37.50	37.39	36.53	37.36	38.46	34.83	35.49	36.28
7	34.68	34.54	35.24	33.24	33.11	33.52	34.48	34.24	34.99	32.54	32.50	33.55	33.90	34.44	35.70	35.12	34.15	36.11	35.10	34.98	36.00	35.05	34.39	35.26	34.36	34.01	35.21	34.04	33.76	34.46	33.50	33.23	34.15
8	36.45	37.12	37.65	35.16	35.73	35.47	35.96	36.39	37.05	34.47	35.29	35.02	36.19	36.94	37.52	37.45	36.96	38.39	37.95	37.87	38.50	37.21	37.29	37.30	36.02	36.45	36.91	36.59	37.11	38.27	35.06	35.52	35.95
9	36.14	37.23	37.79	34.92	35.58	36.18	35.56	36.40	37.05	34.61	35.43	35.74	35.07	37.24	37.25	35.90	36.29	35.84	36.75	37.01	37.58	36.06	36.71	36.46	35.87	36.71	37.49	36.43	37.46	37.98	35.13	36.02	36.32
10	38.07	38.80	38.87	36.97	37.47	37.81	37.37	38.02	38.43	36.69	37.33	37.37	36.81	38.82	38.80	37.66	37.96	37.36	38.48	38.69	39.03	37.90	38.40	37.94	37.66	38.34	38.90	38.11	38.91	39.22	36.93	37.64	37.89
11	38.82	39.32	39.31	37.17	37.45	37.95	38.21	38.63	38.81	37.05	37.39	37.83	37.55	39.12	39.15	38.32	38.51	38.04	39.00	39.15	39.35	38.28	38.72	38.66	38.35	38.80	39.34	38.73	39.19	39.77	37.17	37.63	38.42
12	37.08	38.38	38.68	35.89	36.54	37.52	36.62	37.51	38.49	35.36	36.43	37.23	35.40	37.64	38.33	36.93	36.94	37.73	37.36	37.43	37.98	37.42	37.85	37.87	36.38	37.20	38.17	37.02	38.42	38.90	35.95	36.90	37.35
13	39.64	40.50	40.93	39.27	39.52	40.57	39.68	40.00	40.61	39.10	39.41	40.37	37.27	40.73	40.99	39.99	40.12	40.41	40.70	40.75	41.45	40.12	40.34	40.51	39.99	40.29	41.22	40.28	40.78	41.47	39.31	39.62	40.81
14	37.86	38.31	38.89	37.62	37.87	38.56	37.59	37.91	38.50	37.10	37.69	38.12	36.63	38.48	38.63	38.19	38.27	38.62	38.51	38.57	39.01	38.01	38.29	38.39	37.80	38.21	38.66	38.25	38.53	39.30	36.85	37.21	37.84
15	38.46	38.80	39.19	37.30	37.54	37.98	38.00	38.29	38.74	37.18	37.50	37.78	37.13	38.84	38.90	38.40	38.45	38.58	38.82	38.89	39.25	38.46	38.66	38.56	38.24	38.56	39.09	38.74	38.87	39.07	37.41	37.83	38.28
16	32.65	33.54	33.44	30.95	31.54	31.92	32.22	33.06	33.17	30.83	31.58	31.63	31.82	33.08	32.75	31.65	32.13	31.58	32.58	32.82	33.18	31.47	32.35	32.29	31.78	32.60	32.78	32.18	32.97	34.09	30.28	31.12	32.19
17	33.10	33.79	33.36	31.47	31.87	31.67	32.11	32.65	32.38	31.53	31.97	31.27	32.57	33.69	32.77	31.33	31.95	30.29	32.62	32.93	32.78	31.82	32.54	31.55	32.35	33.06	32.75	32.57	33.38	33.65	31.19	31.91	31.35
18	33.43	34.61	35.06	33.36	33.76	34.64	33.90	34.31	35.22	32.95	33.33	34.44	33.40	34.58	35.49	34.31	34.16	34.59	34.89	34.87	35.46	34.30	34.43	34.98	33.98	34.20	35.27	33.62	34.57	35.40	32.88	33.13	34.26
mean	35.28	36.05	36.38	34.19	34.61	35.15	34.85	35.39	35.99	33.90	34.46	34.87	34.36	35.99	36.25	34.09	34.17	34.20	34.76	34.85	35.42	35.29	35.64	35.64	35.02	35.53	36.21	35.26	35.96	36.71	34.02	34.59	35.31

Table 9: Demosaicking performance (mean CIE Lab error) on IMAX dataset (noise free) with all 11 CFAs

LAB	Bayer CFA			Kodak CFA2.0			Fuji X-Trans			Sony RGBW			Random CFA			Hirakawa 4 color			Condat 6 color			Hao4x4			Hao40			Hao50			Hao60
image	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours
1	5.803	5.418	5.221	7.473	7.075	6.145	6.232	5.857	5.290	7.521	7.083	6.190	6.027	5.453	5.023	6.756	6.795	6.392	6.265	6.355	5.705	7.235	6.813	6.427	6.574	6.295	5.453	7.195	6.727	6.165	8.604	7.856	6.585
2	4.356	4.151	3.869	5.448	5.252	4.562	4.526	4.412	4.048	5.556	5.326	4.699	4.753	4.342	4.075	5.308	5.373	5.074	5.075	5.135	4.743	5.717	5.509	5.228	5.226	5.090	4.473	5.263	5.015	4.852	6.257	5.950	5.253
3	4.561	3.946	3.624	6.359	5.532	4.657	5.162	4.457	3.752	6.741	5.671	4.875	4.980	4.166	3.604	4.942	5.183	4.375	4.767	4.814	4.027	5.828	5.191	4.572	5.565	4.910	4.018	5.409	4.703	4.229	7.394	6.096	5.097
4	1.689	1.330	1.202	2.082	1.876	1.504	1.778	1.533	1.196	2.134	1.912	1.594	2.250	1.371	1.153	1.696	1.732	1.485	1.594	1.576	1.278	1.846	1.705	1.483	1.743	1.604	1.221	1.792	1.503	1.272	2.132	1.889	1.424
5	2.691	2.547	2.388	3.523	3.439	3.079	2.827	2.745	2.535	3.521	3.411	3.090	3.110	2.612	2.529	3.278	3.267	3.079	3.038	3.060	2.752	3.364	3.329	3.228	3.067	3.017	2.697	3.280	3.160	2.933	3.747	3.591	3.183
6	2.343	2.193	2.198	3.081	3.049	2.830	2.500	2.420	2.383	3.068	3.003	2.919	2.690	2.257	2.324	3.062	2.960	3.052	2.793	2.779	2.654	3.110	2.984	3.008	2.787	2.693	2.548	2.967	2.794	2.528	3.582	3.410	3.022
7	3.030	3.046	2.754	4.080	4.122	3.760	3.173	3.241	2.991	4.276	4.318	3.719	3.462	3.203	2.863	3.509	3.857	3.136	3.500	3.571	3.244	3.638	3.860	3.522	3.484	3.621	3.165	3.682	3.800	3.683	4.180	4.314	3.904
8	4.716	4.425	4.147	5.943	5.599	4.929	4.905	4.696	4.318	6.021	5.659	5.025	4.840	4.553	4.224	5.152	5.348	4.734	4.911	5.006	4.487	5.668	5.434	4.981	5.338	5.153	4.532	5.520	5.219	5.027	6.597	6.104	5.334
9	3.310	2.895	2.604	4.425	3.908	3.290	3.430	3.100	2.775	4.502	3.925	3.426	3.699	2.916	2.775	3.901	3.788	3.676	3.544	3.532	3.202	4.263	3.748	3.660	3.790	3.410	2.918	3.988	3.528	3.309	5.050	4.264	3.775
10	3.152	2.907	2.838	4.144	3.887	3.486	3.349	3.133	2.984	4.169	3.901	3.608	3.712	3.001	2.960	3.863	3.787	3.843	3.590	3.569	3.408	4.008	3.751	3.791	3.690	3.465	3.154	3.865	3.607	3.530	4.683	4.256	3.974
11	3.895	3.457	3.518	5.147	4.696	4.143	4.200	3.786	3.636	5.103	4.652	4.220	4.301	3.434	3.361	4.527	4.449	4.536	4.173	4.162	3.919	4.880	4.380	4.203	4.397	4.022	3.594	4.904	4.406	4.150	6.131	5.233	4.639
12	2.312	2.060	1.947	2.933	2.748	2.297	2.511	2.314	2.036	3.080	2.776	2.371	2.930	2.244	2.020	2.684	2.751	2.396	2.527	2.566	2.253	2.780	2.606	2.395	2.669	2.509	2.148	2.597	2.430	2.306	3.266	2.940	2.598
13	1.471	1.355	1.312	1.645	1.631	1.479	1.463	1.444	1.376	1.671	1.654	1.505	1.907	1.351	1.308	1.588	1.595	1.538	1.495	1.499	1.424	1.606	1.605	1.552	1.544	1.533	1.396	1.570	1.544	1.458	1.771	1.758	1.563
14	2.654	2.579	2.442	3.192	3.162	2.873	2.746	2.710	2.569	3.260	3.190	2.974	3.125	2.660	2.608	3.187	3.204	3.081	3.081	3.106	2.934	3.319	3.296	3.155	3.079	3.036	2.830	3.092	3.078	2.916	3.722	3.604	3.311
15	3.260	3.141	2.884	4.270	4.200	3.629	3.354	3.284	3.081	4.275	4.191	3.690	3.753	3.179	3.128	3.956	3.979	3.771	3.735	3.766	3.542	4.025	3.939	3.813	3.689	3.635	3.274	4.049	4.037	4.042	4.880	4.666	4.321
16	6.352	4.826	5.228	8.616	6.872	6.101	7.127	5.456	5.151	8.593	6.809	6.391	6.383	4.864	4.695	6.745	6.463	6.795	6.125	6.046	5.453	8.358	6.388	5.954	7.398	5.893	5.216	8.400	6.480	5.850	####	8.120	7.113
17	5.532	4.968	4.982	8.210	7.483	6.785	6.156	5.544	5.325	8.084	7.330	7.098	6.455	5.427	5.589	7.936	7.460	8.141	7.232	7.124	6.663	8.686	7.541	7.654	7.311	6.568	6.056	7.615	6.751	6.529	9.617	8.250	8.009
18	4.046	3.606	3.548	5.184	4.744	4.081	4.205	3.909	3.515	5.313	4.867	4.170	4.272	3.685	3.320	4.608	4.703	4.389	4.326	4.391	3.961	5.117	4.725	4.246	4.644	4.370	3.706	5.194	4.667	4.364	6.633	5.701	4.982
mean	3.621	3.269	3.150	4.764	4.404	3.868	3.869	3.558	3.276	4.827	4.427	3.976	4.036	3.373	3.198	4.261	4.261	4.083	3.987	4.003	3.647	4.636	4.267	4.049	4.222	3.935	3.467	4.466	4.081	3.841	5.543	4.889	4.338

Table 10: Demosaicking performance (CMSE) on IMAX dataset (noise std=5) with all 11 CFAs

CMSE	Bayer CFA			Kodak CFA2.0			Fuji X-Trans			Sony RGBW			Random CFA			Hirakawa 4 color			Condat 6 color			Hao4x4			Hao40			Hao50			Hao60
Image	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours
1	113.7	105.0	99.93	146.0	138.5	120.8	122.0	114.4	103.1	146.8	138.4	121.2	117.6	102.2	96.21	126.5	126.8	127.3	111.1	112.1	101.0	131.2	122.6	122.1	123.8	116.1	100.1	132.6	120.9	110.3	156.3	145.6	119.8
2	44.20	41.56	40.91	58.73	56.59	51.30	46.35	44.82	42.26	58.82	56.51	50.62	46.29	40.78	38.37	51.50	54.29	51.33	47.95	48.94	45.13	52.32	51.15	48.85	50.91	49.60	43.79	57.25	54.42	53.83	63.91	63.00	54.88
3	64.55	59.41	55.39	96.02	88.75	80.15	77.07	69.00	58.95	104.4	92.07	81.64	71.41	61.96	53.08	67.66	73.74	61.81	65.46	66.13	56.49	70.92	70.54	64.15	79.04	72.53	60.65	78.46	74.16	66.75	101.6	94.03	78.84
4	41.27	34.56	34.18	59.12	52.81	47.82	47.42	39.55	33.85	59.22	52.44	46.96	51.04	34.10	30.91	44.46	47.38	43.77	41.50	41.66	36.43	45.79	44.96	40.70	45.43	41.94	34.75	53.22	48.75	46.79	58.87	54.67	45.34
5	48.53	46.74	45.30	69.52	69.13	62.54	51.64	50.27	47.58	67.52	66.86	60.20	53.69	44.69	44.78	63.92	64.95	64.58	55.56	56.35	50.65	61.53	61.96	60.65	54.94	54.28	47.81	66.67	65.88	65.74	72.66	71.24	63.84
6	30.65	29.83	31.40	46.64	47.39	46.78	32.90	32.41	33.91	44.19	44.99	45.49	32.87	28.57	31.00	45.61	46.07	49.54	39.46	40.13	39.12	44.19	43.86	45.45	37.15	37.16	36.02	46.82	47.12	47.32	53.55	53.71	47.58
7	40.29	41.83	39.85	60.33	61.81	57.19	42.34	44.10	40.57	63.47	65.11	54.00	44.60	41.87	35.76	47.72	55.42	45.30	46.26	48.07	41.68	48.96	53.06	45.92	47.73	50.69	41.76	58.23	61.25	59.25	60.98	65.06	54.27
8	32.85	29.43	29.03	49.19	42.80	42.32	35.70	32.14	30.59	50.24	43.29	42.28	33.85	29.48	27.59	35.44	38.98	34.16	33.17	34.35	31.26	40.94	37.34	35.16	40.18	36.22	32.64	46.64	41.28	39.92	52.21	46.95	41.87
9	33.91	31.60	31.56	50.61	48.09	45.01	37.31	34.81	33.60	49.62	46.93	44.49	38.40	31.05	31.07	44.25	45.52	46.89	40.04	40.47	38.07	44.74	43.32	42.49	40.77	38.86	34.76	47.35	45.41	46.39	51.98	49.91	45.72
10	28.21	27.39	28.70	42.59	40.83	39.20	31.05	29.60	29.48	41.00	39.57	38.02	31.70	26.83	26.80	38.01	39.90	40.78	34.75	35.54	33.65	39.01	38.06	37.15	35.03	33.83	30.83	42.45	40.48	41.95	45.31	43.74	39.89
11	26.58	25.64	26.89	41.91	39.60	37.61	29.03	27.50	27.76	40.03	38.46	35.76	29.62	25.69	25.58	35.36	37.48	38.23	32.96	33.85	32.30	38.20	36.44	34.64	33.47	32.01	29.33	41.18	37.44	38.12	44.64	41.70	36.30
12	30.86	28.95	29.73	46.13	44.89	40.41	33.33	31.43	29.66	45.90	43.58	38.67	36.95	30.15	28.28	41.52	44.03	41.23	38.82	39.72	36.86	40.25	40.63	38.20	38.87	37.71	32.83	45.30	43.70	44.90	48.48	47.85	42.25
13	25.16	25.47	26.31	37.09	38.48	34.87	26.18	26.77	26.23	35.13	36.83	32.47	30.36	24.72	24.15	35.40	37.99	36.87	32.93	34.13	31.55	34.78	36.34	33.56	30.40	31.88	27.94	38.44	40.73	42.25	39.73	42.82	36.44
14	28.75	28.85	28.94	40.81	40.82	37.24	30.56	30.42	29.43	39.78	39.43	35.71	32.35	28.07	27.45	37.71	40.23	38.59	35.55	36.66	33.98	38.76	38.92	36.24	34.71	34.77	31.34	42.20	43.09	43.48	45.39	46.50	40.32
15	27.40	27.22	27.74	41.54	40.35	37.75	29.43	28.77	28.35	39.56	38.85	35.74	30.77	26.72	26.39	36.06	38.49	37.47	33.95	34.95	32.71	37.80	37.19	35.01	33.61	33.00	29.89	41.11	39.65	40.77	43.87	42.40	37.56
16	53.43	47.32	49.27	81.61	73.79	70.14	58.28	51.10	51.52	80.80	72.11	71.29	61.00	50.20	53.10	70.34	68.55	73.56	61.00	60.35	57.42	74.91	66.48	65.65	67.07	59.77	57.45	71.93	64.04	58.69	93.17	81.31	68.02
17	49.88	46.32	50.61	75.76	71.53	76.05	59.15	54.79	59.09	72.84	69.06	78.30	54.15	46.28	53.32	74.45	70.75	92.19	61.16	60.00	62.30	71.15	65.12	74.23	61.70	56.57	59.35	68.50	63.17	63.94	81.59	74.77	79.91
18	47.67	41.35	40.67	59.22	56.11	50.05	45.59	43.11	39.54	59.97	57.54	48.65	47.82	40.79	36.71	50.65	54.17	50.98	46.59	47.81	43.82	52.70	51.90	47.13	49.85	48.93	41.64	60.81	55.83	54.30	65.63	64.53	53.76
mean	42.66	39.92	39.80	61.27	58.46	54.29	46.41	43.61	41.42	61.07	57.89	53.42	46.92	39.68	38.36	52.59	54.71	54.14	47.68	48.40	44.69	53.79	52.22	50.40	50.26	48.10	42.94	57.73	54.85	53.59	65.55	62.77	54.81

Table 11: Demosaicking performance (CPSNR) on IMAX dataset (noise std=5) with all 11 CFAs

CPSNR	Bayer CFA			Kodak CFA2.0			Fuji X-Trans			Sony RGBW			Random CFA			Hirakawa 4 color			Condat 6 color			Hao4x4			Hao40			Hao50			Hao60
image	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours
1	27.57	27.92	28.13	26.49	26.72	27.31	27.27	27.55	28.00	26.46	26.72	27.29	27.43	28.03	28.30	27.11	27.10	27.08	27.67	27.64	28.09	26.95	27.25	27.26	27.20	27.48	28.13	26.90	27.31	27.71	26.19	26.50	27.35
2	31.68	31.94	32.01	30.44	30.60	31.03	31.47	31.62	31.87	30.44	30.61	31.09	31.48	32.03	32.29	31.01	30.78	31.03	31.32	31.23	31.59	30.94	31.04	31.24	31.06	31.18	31.72	30.55	30.77	30.82	30.08	30.14	30.74
3	30.03	30.39	30.70	28.31	28.65	29.09	29.26	29.74	30.43	27.94	28.49	29.01	29.59	30.21	30.88	29.83	29.45	30.22	29.97	29.93	30.61	29.62	29.65	30.06	29.15	29.53	30.30	29.18	29.43	29.89	28.06	28.40	29.16
4	31.97	32.75	32.79	30.41	30.90	31.34	31.37	32.16	32.83	30.41	30.93	31.41	31.05	32.80	33.23	31.65	31.37	31.72	31.95	31.93	32.52	31.52	31.60	32.04	31.56	31.90	32.72	30.87	31.25	31.43	30.43	30.75	31.57
5	31.27	31.43	31.57	29.71	29.73	30.17	31.00	31.12	31.36	29.84	29.88	30.33	30.83	31.63	31.62	30.07	30.01	30.03	30.68	30.62	31.08	30.24	30.21	30.30	30.73	30.78	31.34	29.89	29.94	29.95	29.52	29.60	30.08
6	33.27	33.38	33.16	31.44	31.37	31.43	32.96	33.02	32.83	31.68	31.60	31.55	32.96	33.57	33.22	31.54	31.50	31.18	32.17	32.10	32.21	31.68	31.71	31.56	32.43	32.43	32.57	31.43	31.40	31.38	30.84	30.83	31.36
7	32.08	31.92	32.13	30.33	30.22	30.56	31.86	31.69	32.05	30.10	29.99	30.81	31.64	31.91	32.60	31.34	30.69	31.57	31.48	31.31	31.93	31.23	30.88	31.51	31.34	31.08	31.92	30.48	30.26	30.40	30.28	30.00	30.79
8	32.97	33.44	33.50	31.21	31.82	31.86	32.60	33.06	33.28	31.12	31.77	31.87	32.84	33.44	33.72	32.64	32.22	32.80	32.92	32.77	33.18	32.01	32.41	32.67	32.09	32.54	32.99	31.44	31.97	32.12	30.95	31.41	31.91
9	32.83	33.13	33.14	31.09	31.31	31.60	32.41	32.71	32.87	31.17	31.42	31.65	32.29	33.21	33.21	31.67	31.55	31.42	32.11	32.06	32.33	31.62	31.76	31.85	32.03	32.24	32.72	31.38	31.56	31.47	30.97	31.15	31.53
10	33.63	33.75	33.55	31.84	32.02	32.20	33.21	33.42	33.44	32.00	32.16	32.33	33.12	33.84	33.85	32.33	32.12	32.03	32.72	32.62	32.86	32.22	32.33	32.43	32.69	32.84	33.24	31.85	32.06	31.90	31.57	31.72	32.12
11	33.88	34.04	33.83	31.91	32.15	32.38	33.50	33.74	33.70	32.11	32.28	32.60	33.42	34.03	34.05	32.65	32.39	32.31	32.95	32.84	33.04	32.31	32.51	32.74	32.88	33.08	33.46	31.98	32.40	32.32	31.63	31.93	32.53
12	33.24	33.51	33.40	31.49	31.61	32.07	32.90	33.16	33.41	31.51	31.74	32.26	32.45	33.34	33.62	31.95	31.69	31.98	32.24	32.14	32.47	32.08	32.04	32.31	32.23	32.37	32.97	31.57	31.73	31.61	31.28	31.33	31.87
13	34.12	34.07	33.93	32.44	32.28	32.71	33.95	33.85	33.94	32.67	32.47	33.02	33.31	34.20	34.30	32.64	32.33	32.46	32.95	32.80	33.14	32.72	32.53	32.87	33.30	33.10	33.67	32.28	32.03	31.87	32.14	31.81	32.51
14	33.54	33.53	33.52	32.02	32.02	32.42	33.28	33.30	33.44	32.13	32.17	32.60	33.03	33.65	33.75	32.37	32.09	32.27	32.62	32.49	32.82	32.25	32.23	32.54	32.73	32.72	33.17	31.88	31.79	31.75	31.56	31.46	32.08
15	33.75	33.78	33.70	31.95	32.07	32.36	33.44	33.54	33.61	32.16	32.24	32.60	33.25	33.86	33.92	32.56	32.28	32.39	32.82	32.70	32.98	32.36	32.43	32.69	32.87	32.95	33.38	31.99	32.15	32.03	31.71	31.86	32.38
16	30.85	31.38	31.20	29.01	29.45	29.67	30.48	31.05	31.01	29.06	29.55	29.60	30.28	31.12	30.88	29.66	29.77	29.46	30.28	30.32	30.54	29.39	29.90	29.96	29.87	30.37	30.54	29.56	30.07	30.45	28.44	29.03	29.80
17	31.15	31.47	31.09	29.34	29.59	29.32	30.41	30.74	30.42	29.51	29.74	29.19	30.79	31.48	30.86	29.41	29.63	28.48	30.27	30.35	30.19	29.61	29.99	29.43	30.23	30.60	30.40	29.77	30.13	30.07	29.01	29.39	29.10
18	31.35	31.97	32.04	30.41	30.64	31.14	31.54	31.78	32.16	30.35	30.53	31.26	31.33	32.03	32.48	31.08	30.79	31.06	31.45	31.34	31.71	30.91	30.98	31.40	31.15	31.24	31.94	30.29	30.66	30.78	29.96	30.03	30.83
mean	32.18	32.43	32.41	30.55	30.73	31.04	31.83	32.07	32.26	30.59	30.79	31.14	31.73	32.47	32.60	30.92	30.75	30.80	31.35	31.28	31.63	31.09	31.19	31.38	31.42	31.58	32.06	30.74	30.94	31.00	30.26	30.41	30.98

Table 12: Demosaicking performance (mean CIE Lab error) on IMAX dataset (noise std=5) with all 11 CFAs

LAB	Bayer CFA			Kodak CFA2.0			Fuji X-Trans			Sony RGBW			Random CFA			Hirakawa 4 color			Condat 6 color			Hao4x4			Hao40			Hao50			Hao60
image	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours	Var	Rad	Ours
1	7.239	6.758	6.527	9.450	8.678	7.792	7.642	7.120	6.533	9.300	8.607	7.619	7.380	6.772	6.275	8.328	8.553	8.019	7.928	8.088	7.408	9.124	8.479	8.009	8.244	7.779	6.831	9.864	8.835	8.470	10.86	9.705	8.373
2	7.625	7.161	6.755	9.799	8.891	7.928	7.717	7.348	6.706	9.638	8.873	7.652	7.886	7.298	6.712	8.738	9.242	8.423	8.670	8.914	8.176	9.900	9.096	8.470	8.915	8.375	7.300	10.79	9.562	9.502	11.08	10.01	8.880
3	6.737	5.830	5.449	9.155	7.688	6.864	7.217	6.203	5.494	9.327	7.756	6.734	6.980	5.971	5.310	7.054	7.507	6.519	6.960	7.098	6.281	8.551	7.427	6.725	7.933	6.886	5.866	9.159	7.572	7.367	10.41	8.526	7.453
4	2.480	2.327	2.152	3.188	3.078	2.690	2.603	2.464	2.096	3.182	3.078	2.576	2.828	2.329	2.050	2.871	3.051	2.664	2.833	2.884	2.528	2.998	3.017	2.687	2.724	2.716	2.214	3.196	3.109	3.018	3.366	3.321	2.783
5	4.300	4.209	3.955	5.621	5.500	4.991	4.380	4.341	3.967	5.498	5.424	4.738	4.557	4.244	3.950	5.231	5.483	5.019	5.111	5.248	4.773	5.477	5.454	5.134	4.899	4.881	4.281	5.897	5.795	5.716	6.159	6.046	5.376
6	4.564	4.491	4.314	5.982	5.883	5.407	4.620	4.608	4.305	5.791	5.760	5.135	4.713	4.499	4.254	5.731	5.997	5.601	5.591	5.738	5.280	5.918	5.852	5.518	5.238	5.249	4.673	6.627	6.422	6.342	6.773	6.683	5.860
7	5.881	5.525	5.097	7.753	7.114	6.436	5.970	5.655	5.093	7.756	7.214	6.034	6.123	5.646	4.991	6.471	7.139	6.010	6.552	6.799	6.063	7.352	6.982	6.274	6.703	6.382	5.428	8.324	7.538	7.451	8.251	7.677	6.715
8	13.00	11.00	10.15	16.82	13.41	11.76	12.92	11.07	9.82	16.31	13.33	11.02	12.89	11.07	9.88	12.80	13.77	12.02	12.94	13.37	11.96	16.52	13.48	12.21	14.68	12.43	10.50	18.53	14.39	14.11	18.55	14.80	12.75
9	6.080	5.495	5.184	8.138	7.010	6.277	6.158	5.623	5.131	7.943	6.912	6.044	6.288	5.500	5.097	6.824	7.123	6.619	6.655	6.812	6.252	7.816	6.923	6.542	6.943	6.276	5.467	8.955	7.527	7.529	9.287	7.781	7.004
10	7.317	6.682	6.431	9.677	8.349	7.585	7.430	6.785	6.272	9.335	8.228	7.236	7.604	6.720	6.239	8.114	8.564	7.953	8.013	8.233	7.564	9.203	8.239	7.759	8.412	7.609	6.688	11.09	9.165	9.191	11.04	9.298	8.411
11	9.098	7.722	7.581	12.16	9.697	8.829	9.308	7.881	7.411	11.64	9.540	8.440	9.189	7.704	7.221	9.349	9.840	9.265	9.139	9.379	8.695	11.46	9.454	8.850	10.34	8.707	7.723	14.42	10.64	10.62	14.34	10.78	9.680
12	4.550	4.300	4.122	6.018	5.517	4.913	4.706	4.433	4.014	5.920	5.438	4.636	4.922	4.409	3.992	5.346	5.682	5.081	5.263	5.407	4.910	5.654	5.395	4.947	5.220	4.988	4.297	6.788	6.120	6.182	6.861	6.254	5.558
13	2.734	2.758	2.634	3.404	3.509	3.148	2.713	2.799	2.555	3.324	3.454	2.892	2.972	2.744	2.503	3.364	3.620	3.249	3.342	3.460	3.130	3.334	3.488	3.187	3.024	3.174	2.707	3.856	3.997	4.027	3.813	4.047	3.481
14	6.269	6.130	5.789	7.904	7.529	6.700	6.334	6.226	5.666	7.765	7.453	6.402	6.502	6.159	5.677	7.350	7.825	7.095	7.393	7.625	6.953	7.901	7.603	6.996	7.234	7.039	6.140	8.837	8.322	8.230	9.005	8.546	7.488
15	8.444	7.714	7.328	11.10	9.598	8.516	8.431	7.762	7.109	10.71	9.444	8.048	8.614	7.707	7.117	9.238	9.777	8.908	9.153	9.393	8.518	10.48	9.328	8.633	9.576	8.670	7.534	13.11	10.64	10.62	12.88	10.76	9.570
16	9.143	7.145	7.347	12.38	9.418	8.733	9.787	7.534	7.205	12.02	9.254	8.617	9.021	7.145	6.829	9.157	9.306	9.167	8.680	8.787	8.155	11.80	9.041	8.497	10.49	8.310	7.503	13.68	9.895	9.630	15.67	10.80	9.731
17	8.910	8.075	8.019	12.36	10.74	10.16	9.449	8.475	8.192	12.00	10.53	10.12	9.581	8.349	8.258	10.88	10.92	11.22	10.41	10.51	10.07	12.57	10.77	10.74	10.94	9.665	8.990	13.11	11.08	11.13	14.31	11.87	11.44
18	6.597	5.970	5.770	8.560	7.593	6.751	6.652	6.121	5.546	8.462	7.609	6.443	6.623	5.983	5.411	7.305	7.742	7.040	7.168	7.369	6.700	8.407	7.592	6.891	7.478	6.934	5.924	9.553	8.320	8.219	10.33	8.883	7.821
mean	6.720	6.072	5.811	8.859	7.733	6.971	6.891	6.247	5.729	8.663	7.661	6.688	6.926	6.125	5.654	7.452	7.841	7.215	7.322	7.507	6.856	8.581	7.646	7.115	7.722	7.004	6.115	9.766	8.274	8.186	10.17	8.654	7.687