Title :
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
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;
Conference_Titel :
Image Processing (ICIP), 2011 18th IEEE International Conference on
Conference_Location :
Brussels
Print_ISBN :
978-1-4577-1304-0
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2011.6116648
