An adaptive algorithm for image restoration using combined penalty functions

In this paper, we present an adaptive gradient based method to restore images degraded by the effects of both noise and blur. The approach combines two penalty functions. The first derivative of the Canny operator is employed as a roughness penalty function to improve the high frequency information...

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Bibliographic Details
Published inPattern recognition letters Vol. 27; no. 12; pp. 1336 - 1341
Main Authors Zhu, Daan, Razaz, Moe, Fisher, Mark
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.09.2006
Subjects
Combined penalty function
Gradient descent
Image restoration
Penalized likelihood
Regularization
Combined penalty function
Gradient descent
Image restoration
Regularization
Penalized likelihood
Online AccessGet full text
ISSN0167-8655
1872-7344
DOI10.1016/j.patrec.2006.01.009

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Summary:In this paper, we present an adaptive gradient based method to restore images degraded by the effects of both noise and blur. The approach combines two penalty functions. The first derivative of the Canny operator is employed as a roughness penalty function to improve the high frequency information content of the image and a smoothing penalty term is used to remove noise. An adaptive algorithm is used to select the roughness and smoothing control parameters. We evaluate our approach using the Richardson–Lucy EM algorithm as a benchmark. The results highlight some of the difficulties in restoring blurred images that are subject to noise and show that in this case an algorithm that uses a combined penalty function is able to produce better quality results.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2006.01.009