Course information

ECS605U/ECS776P - Image Processing


The main purpose of this course is to provide an introduction to the knowledge and methodologies for digital image processing.


Some mathematical background, such as calculus, complex arithmetic, statistics, linear algebra, basic understanding of signal processing (Fourier transform), some programming experience.



This course gives students an introduction to digital image processing and uses a programming language Java to implement simple applications in low level image processing. Areas covered include image representation, image sampling and display, and image transforms and image enhancement using point and spatial operations. Also considered are image processing methods such as convolution, frequency filtering and image restoration, compression and segmentation.


One 2 hour and 30 minute examination for 80% and 20% assessed coursework



Digital Image Processing (Global Edition, 4th Edition), by Rafael C. Gonzalez, and Richard E. Woods, 1024 pages, Pearson, 2017, ISBN13: 9781292223049, ISBN10: 1292223049.

Reference book:

Digital Image Processing Using MATLAB, by Rafael C. Gonzalez,‎ Richard E. Woods,‎ Steven L. Eddins, 827 pages, Gatesmark Publishing; 2nd edition (2009), ISBN-10: 0982085400, ISBN-13: 978-0982085400.


Reference journals:

IEEE Trans. Image Processing

IEEE Trans. Signal Processing

IEEE Trans. Medical Imaging

Computer Vision and Image Understanding (CVIU)

Graphical Modeling and Image Processing

Computer Vision, Graphics and Image Processing (CVGIP)


Reference conference proceedings:

British Machine Vision Conference (BMVC)

International Conference on Image Processing (ICIP)

IEEE International Conference on Computer Vision (ICCV)

IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)

Webpages and Mailing Lists

The Course - :

The Course - QM+ :

The Course - Image Processing labs:

The Course - Lecture notes (image processing):

The Course - Coursework:

The Course - Mailing lists:;

Test images (RAW, BMP, and TIF formats):

The text book website:

Past exam papers are available on QM+.



Image processing: Pengwei Hao (

Labs and exercises: Zhaoyang Xu (, Bingqing Guo (, Xindi Zhang ( .

Schedule (tentative)






Lab Session

Week 1


Chapter 1

No Lab Session

Week 2

Image representation, sampling and quantization

Chapter 2

Session 1

Week 3

Point processing for image enhancement

Chapter 3

Session 2

Week 4

image histogram

Chapter 3

Session 3

Week 5

Image filtering/convolution, smoothing and sharpening

Chapter 3

Session 4

Week 6

Fourier transform

Chapter 4

Session 5

Week 7

Review and Feedback Week

Session 6

Week 8

Image enhancement in frequency domain

Chapter 4

Session 7

Week 9

Image restoration: degradation models, inverse filters, deconvolution

Chapter 5

Session 8

Week 10

Image segmentation: edge detection, thresholding

Chapter 10

Session 9

Week 11

Image compression: information theory, Huffman coding, run-length coding, lossy compression, compression standards

Chapter 8

Coursework Assessment

Week 12

Revision (exercises)


Coursework Assessment