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DA-IICT
1. Course Title Image Representation and Analysis
2. Credit Structure Lecture hours per week: 3
3. Course Code IT530
Tutorial hours per week: 0
Practical hours per week: 0
Total Credits: 3
4. Program/Semester M Tech Semester III
5. Category Core / Group core / Technical Elective / Open Elective / Science Elective
6. Prerequisite
courses
7. Foundation for
Technical Elective
Pattern Recognition, Digital Image Processing
8. Abstract Content This course will cover methods for representation of images motivated by
statistical properties of natural images and the applications of these
representations in solving problems in image analysis including noise removal,
deblurring, inpainting, compression and classification. The course will also
cover some topics in compressive sensing and a discussion of some practical
compressive imaging systems.
Suggested Text
book(s)
Optional
Papers related to the topics being discussed (available online) will be the
primary resource for students. Suggested textbooks include:
Independent Components Analysis, Hyvarinen, Karhunen and Oja,
Wiley Series
Compressive Sensing: Theory and Applications, Eldar and Kutyniok,
Cambridge

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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