Adaptive regularization for multiple image restoration using an extended Total Variations approach

Author

Kitchener, Matthew Andrew ; Bouzerdoum, Abdesselam ; Phung, Son Lam

Author_Institution

Sch. of Electr., Comput. & Telecommun. Eng., Univ. of Wollongong, Wollongong, NSW, Australia

fYear

2011

fDate

11-14 Sept. 2011

Firstpage

697

Lastpage

700

Abstract

In this paper a Variational Inequality method for multiple in- put, multiple output image restoration is presented using an extended Total Variations (TV) regularizer. This approach calculates an adaptive regularization parameter for each image based on their respective degradations. The proposed ex- tended Total Variations regularizer combines both intra-image and inter-image pixel information for improved restoration performance. Hyperparameters for controlling this new TV measure are calculated using a Bayesian joint maximum a posteriori approach.

Keywords

Bayes methods; image restoration; maximum likelihood estimation; parameter estimation; Bayesian joint maximum a posteriori approach; adaptive regularization parameter; extended TV regularizer; extended total variations approach; extended total variations regularizer; hyperparameters; inter-image pixel information; intra-image pixel information; multiple input multiple output image restoration; variational inequality method; Bayesian methods; Image restoration; Noise; Noise measurement; TV; Vectors; Bayesian; Image Restoration; Multiple Image; Total Variations;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing (ICIP), 2011 18th IEEE International Conference on

Conference_Location

Brussels

ISSN

1522-4880

Print_ISBN

978-1-4577-1304-0

Electronic_ISBN

1522-4880

Type

conf

DOI

10.1109/ICIP.2011.6116648

Filename

6116648