Digital Image Processing


The course aims at providing students with basic knowledge and practice about the use of computer algorithms to perform image processing on digital images. The two main objectives attached to Digital Image Processing (DIP) are to improve the visual quality of images and to automatically extract semantic information from visual data (e.g. object recognition).

Teaching and Learning Methods: Each session is split into two parts: a 1.5-hour lecture and a 1.5-hour lab.

 Course Policies:  It is mandatory to attend the lab. sessions.

  • Book: ANIL K. Jain. Fundamentals of Digital Image Processing. PEARSON, 569 p.


It would be good if you already have some knowledge about signal processing and Matlab, but it is not mandatory.

The course includes a review of some important points of signal processing for digital images such as sampling, quantization, Fourier transform, filtering (noise reduction, edge detection), etc.; some key elements in human vision, color and image quality; the presentation of some popular image processing tools and techniques such as the Hough transform, mathematical morphology, optical flow and finally some basic notions in stereovision and 3D.
Learning Outcomes: 
  • Discover the key techniques used in image and video processing.
  • Become familiar with classical image processing tools and software.

Nb hours: 21.00


  • Lab. reports (20% of the final grade)
  • Final Exam (80% of the final grade)