Blind Multichannel Image Deconvolution and Optimum Sparse Approximations

The second problem is one of computing maximally sparse elements of a convex, compact set. This problem arises in a wide range of engineering applications, including regularization of ill-posed problems, design of digital filters with few non-zero coefficients and the computation of sparse approximate solutions to inverse problems. Because the problem is N-P complete, there exists a need to develop heuristic techniques that work well for specific problems. Our contribution is the development of a new class of iterative algorithms for identifying sparse elements of the convex and compact set. We show that the algorithm has good convergence properties through a detailed theoretical analysis and demonstrate its performance on some examples.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Harikumar, G.

Contributors dc:contributor

• Bresler, Yoram

• Engineering, Electronics and Electrical

Language dc:language

eng

Identifier

(MiAaPQ)AAI9737127

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81183