ECE 188: Image and video restoration

Course outline

I Introduction to inverse problems in image/video restoration contexts:denoising, deblurring, super-resolution, tomography, compressed sensing

I Fundamentals of linear/local filtering:maximum likelihood, spatial averaging, heat equation, low-pass and Wiener filtering

I Basic of non-linear filtering:signal adaptation, maximum a posteriori, wavelets and sparsity, non-locality, patches

I Towards advanced filtering:dictionary learning, convex and non-convex optimization, parameter selection

I Lab work covering the implementation of such techniques in Matlab.

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Prerequisites

I Linear algebra (MATH 18)

I Differential calculus (MATH 20C)

I Probability and statistics (ECE 109)

I Fourier transform (ECE 161A)

I Basics of optimization (ECE 174)

I Matlab programming

Project – blind restoration challenge

I Analysis of a corrupted corpus of images

I Mathematical modeling of the inverse problem

I Elaboration of a restoration technique

I Implementation in Matlab

I Detailed technical report with bibliography

I Evaluation: originality/performance/quality