Course details of CS 754 - Advanced Image Processing

Course Name Advanced Image Processing
Total Credits 6
Type T
Lecture 3
Tutorial 0
Practical 0
Selfstudy 0
Half Semester N
Prerequisite 0.0
Text Reference We will extensively refer to the following textbooks, besides a number of research papers from journals such as IEEE Transactions on Image Processing, IEEE Transactions on Signal Processing, and IEEE Transactions on Pattern Analysis and Machine Intelligence: 1. "Natural Image Statistics" by Aapo Hyvarinen, Jarmo Hurri and Patrick Hoyer, Springer Verlag 2009 (h ttp://www.naturalimagestatistics.net/ - freely downloadable online) 2. "A Mathematical Introduction to Compressive Sensing" by Simon Foucart and Holger Rauhut, Birkhauser,2013 ( http://www.springer.com/us/book/9780817649470)
Description (1) Image transforms and statistics of natural images ● survey of statistical properties of image transform coefficients ● implications of these statistics for important image processing applications such as denoising, compression, source separation, deblurring and image forensics ● non-local self-similarity in images (2) Dictionary learning and sparse representations in image processing ● Overview of Principal Components Analysis (PCA), Singular Value Decomposition (SVD) and Independent Components Analysis (ICA); PCA, SVD and ICA in the context of image processing ● Sparse PCA ● Concept of overcomplete dictionaries ● Greedy pursuit algorithms: matching pursuit (MP), orthogonal matching pursuit (OMP) and basis pursuit (BP) ● Popular dictionary learning techniques: Method of Optimal Directions (MOD), Unions of Orthonormal Bases, K-SVD, Non-negative sparse coding along with applications in image compression, denoising, inpainting and deblurring ● Sparsity-seeking algorithms: iterative shrinkage and thresholding (ISTA) (3) Compressed Sensing (CS) ● Concept and need for CS ● Theoretical treatment: concept of coherence, null-space property and restricted isometry property, proof of a key theorem in CS ● Algorithms for CS (covered in part 2) and some key properties of these algorithms ● Applications of CS: Rice Single Pixel Camera and its variants, Video compressed sensing, Color and Hyperspectral CS, Applications in Magnetic Resonance Imaging (MRI), Implications for Computed Tomography ● CS under Forward Model Perturbations: a few key results and their proofs as well as applications ● Designing Forward Models for CS ● Low-rank matrix estimation and Robust Principal Components Analysis: concept and application scenarios in image processing, statement of some key theorems, and proof of one important theorem
Last Update 08-11-2016 12:16:06.333343