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. 0 0.2 0.4 0.6 0.8 1 −0.2 0 0.2 0.4 0.6 0.8 1 Position index i Value x i Interval ± σ Vector x 0 Threshold λ = + ? 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