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Detailed Course Contents
Topic Name Content (2 -3 lines per 4 – 6 lectures) No. of
lectures
(tentati
ve)
Statistics of natural
images
Statistics of image Fourier or wavelet coefficients, relationship
between Fourier bases and principal components of natural images
Readings:
(i) Huang and Mumford, “Statistics of Natural Images and Models”
(ii) Hyvarinen et al, “Independent Components Analysis” (book)
Sparse coding Sparse coding of images with applications to image denoising
Readings:
(i) Hyvarinen et al, “Image Denoising by Sparse Code Shrinkage”
(ii) Rajashekhar and Simoncelli, “Multiscale denoising of
photographic images”
Dictionary learning Dictionary learning for efficient image representation: applications
in compression, deblurring, inpainting and classification
Readings:
(i) Lewicki and Sejnowski, “Learning Overcomplete
Representations”
(ii) Aharon et al, “KSVD-An Algorithm for Designing of
Overcomplete Dictionaries for Sparse Representation”
(iii) Mairal et al, “Discriminative Sparse Image Models for Class-
Specific Edge Detection and Image Interpretation”
Compressive Sensing Overview of basic set of results, proof of one key result, examples
of practical compressive imaging systems: Rice single-pixel
camera, and time-domain coded multiplexing for compressive
video
Non-local selfsimilarity
of images
Non-local means, collective processing of image patches (spatially
varying PCA, simultaneous sparse coding)
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