Home - Log in

Image Processing

79 Image Processing Physical engineering and embedded systems S9
Lessons : 4 h TD : 0 h TP : 24 h Project : 0 h Total : 28 h
Co-ordinator : Loic SIMON
Prerequisite
Signal processing,
Algorithms
Course Objectives
1) Expose the classical modeling of images and acquisition noise as stochastic processes
2) Introduce standard principles of classical algorithms (filtering, inverse problems, …)
Syllabus
Part 1: modelling
- Image capture chain (simplified model of CCDs, transfer function, Bayer pattern, natural vignetting)
- stochastic processes (reminders on random variables, modelling of distributions, stochastic processes, invariance)
- acquisition noise (internal / external noise, shot-noise, quantification, noise, other noise models)
- color spaces (RGB, CMYK, HSV, HSL)

Part 2: algorithms
- functional and technical specifications (task goals, constraints, complexity)
- examples (integral images, histogram equalization)
- linear filtering (taxonomy, FFT, application to contour extraction and sharpening)
- Image restoration (denoising, non-blind deconvolution)
- Inverse problems (formulation, gradient descent, handling of outliers)
- Interpolation
- Short introduction to deep learning
Practical work (TD or TP)
1) Basic image processing operators in Matlab
2) Linear filtering and FFT: application to image restauration
3) Image segmentation
4) Case studies: medical imaging; shape recognition;
Acquired skills
- General skills (stochastic modelling, algorithmic notions, optimization)
- specific skills (image capture chain, image processing standard formulations, efficient solutions)
Bibliography
Gonzales, Rafael C., and Richard E. Woods. "Digital image processing." (2002).

© 2024 - ENSICAEN ( Legal Notices - Credits )