Image Processing

Nowadays, image processing is ubiquitous in information technology: medicine, biology, agriculture, entertainment, culture, measurement, mechanics, etc.

Image processing involves applying mathematical transformations to images in order to modify their appearance or extract information from them. More generally, image processing aims to manipulate the underlying information contained in an image. While it has long been performed using electronic circuits, image processing is now carried out almost exclusively digitally, i.e., using algorithms generally programmed with an imperative language (C, C++, Java, Python, etc.).

This teaching unit aims to provide a solid foundation in image processing. It covers, among other things, image formation and acquisition, colorimetric transformations, morphological operations, geometric transformations, compression, frequency transformations, recognition and matching techniques, and an introduction to deep learning methods. The courses are supplemented by supporting videos.

The teaching unit consists mainly of 11 lectures covering the basics of the main areas of image processing and three practical sessions, with topics to be chosen from six proposals. Students can choose to carry out the work on images they bring in that are relevant to their field of study.

The objective of this module is to provide students with the fundamentals of image processing, enabling them to 1) understand the operations performed by image processing software, 2) read articles on image processing, 3) develop their own applications, and 4) continue their education in this field on their own.

Fundamentals of signal processing.

Some programming knowledge in one language.

Written 60%, practical work (based on report) 40%

• Image formation, digital image, luminance image, color image, etc.
• Image acquisition technology.
• Mathematical morphology
• Convolution kernels, interpolation kernel: discrete representation of transformation defined in the continuous domain.
• Derivation of images.
• Extraction of contours and specific points.
• Fourier transform on images.
• Image filtering, digital convolution, and noise reduction.
• Correlation and distances between images.
• Principle of image compression.
• Geometric transformations.

CM: 4:30 p.m.

 Practical work: 9:00 a.m.