Blind Image Deconvolution : Methods and Convergence / by Subhasis Chaudhuri, Rajbabu Velmurugan, Renu Rameshan.

Format:

Book

Author/Creator:

Chaudhuri, Subhasis, author.

Velmurugan, Rajbabu, author.

Rameshan, Renu, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Optical data processing.

Signal processing.

Image processing.

Speech processing systems.

Image Processing and Computer Vision.

Signal, Image and Speech Processing.

Local Subjects:

Image Processing and Computer Vision.

Signal, Image and Speech Processing.

Physical Description:

1 online resource (XV, 151 pages) : 33 illustrations, 16 illustrations in color

Edition:

First edition 2014.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2014.

System Details:

text file PDF

Summary:

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 sparsity based solution is also provided for blind deconvolution, by using image priors having a cost that increases with the amount of blur, which is another way to prevent trivial solutions in joint estimation. This book will be a highly useful resource to the researchers and academicians in the specific area of blind deconvolution.

Contents:

Introduction

Mathematical Background

Blind Deconvolution Methods: A Review

MAP Estimation: When Does it Work?

Convergence Analysis in Fourier Domain

Spatial Domain Convergence Analysis

Sparsity-based Blind Deconvolution

Conclusions and Future Research Directions.

Other Format:

Printed edition:

ISBN:

978-3-319-10485-0

9783319104850

Access Restriction:

Restricted for use by site license.