Poissonian image deconvolution via sparse and redundant representations and framelet regularization
type: Item
instance of: scholarly article
title: Poissonian image deconvolution via sparse and redundant representations and framelet regularization
zbMATH Open document ID: 1407.94025
DOI: 10.1155/2014/917040
author: Yu Shi
author: Houzhang Fang
author: Guo-You Wang
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: Poissonian image deconvolution is a key issue in various applications, such as astronomical imaging, medical imaging, and electronic microscope imaging. A large amount of literature on this subject is analysis-based methods. These methods assign various forward measurements of the image. Meanwhile, synthesis-based methods are another well-known class of methods. These methods seek a reconstruction of the image. In this paper, we propose an approach that combines analysis with synthesis methods. The method is proposed to address Poissonian image deconvolution problem by minimizing the energy functional, which is composed of a sparse representation prior over a learned dictionary, the data fidelity term, and framelet based analysis prior constraint as the regularization term. The minimization problem can be efficiently solved by the split Bregman technique. Experiments demonstrate that our approach achieves better results than many state-of-the-art methods, in terms of both restoration accuracy and visual perception.
Mathematics Subject Classification ID: 94A08
Mathematics Subject Classification ID: 65R10
Mathematics Subject Classification ID: 65R32
Mathematics Subject Classification ID: 65T60
zbMATH DE Number: 7017606
Wikidata QID: Q59070740
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/917040
OpenAlex ID: W2040644190
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description: scientific article; zbMATH DE number 7017606