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Sparsity-Based Blind Deconvolution by Chaudhuri, Subhasis; Velmurugan, Rajbabu; Rameshan, Renu is a scholarly article available to read on EtoBox.

What is Sparsity-Based Blind Deconvolution about?

Blind deconvolution is a classical image processing problem which has been investigated by a large number of researchers over the last four decades. The purpose of this monograph is not to propose yet another method for blind image restoration. Rather the basic issue of deconvolvability has been explored from a theoretical view point. Some authors claim very good results while quite a few claim that blind restoration does not work. The authors clearly detail when such methods are expected to work and when they will not. In order to avoid the assumptions needed for convergence analysis in the Fourier domain, the authors use a general method of convergence analysis used for alternate minimization based on three point and four point properties of the points in the image space. The authors prove that all points in the image space satisfy the three point property and also derive the conditions under which four point property is satisfied. This provides the conditions under which alternate minimization for blind deconvolution converges with a quadratic prior. Since the convergence properties depend on the chosen priors, one should design priors that avoid trivial solutions. Hence, a spar

Author

Chaudhuri, Subhasis; Velmurugan, Rajbabu; Rameshan, Renu

Publisher

Springer International Publishing : Imprint: Springer

Published

2014

Language

EN

ISBN

9783319104843