An MMSE approach to nonlocal image denoising: Theory and practical implementation
DC Field | Value | Language |
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dc.contributor.author | Lee, Chul | - |
dc.contributor.author | Lee, Chulwoo | - |
dc.contributor.author | Kim, Chang-Su | - |
dc.date.accessioned | 2021-09-06T21:47:36Z | - |
dc.date.available | 2021-09-06T21:47:36Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2012-04 | - |
dc.identifier.issn | 1047-3203 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/108843 | - |
dc.description.abstract | A nonlocal minimum mean square error (MMSE) image denoising algorithm is proposed in this work. Based on the Bayesian estimation theory, we first derive that the conventional nonlocal means filter is an MMSE estimator in the special case of noise-free nonlocal neighbors. Then, we develop the nonlocal MMSE denoising filter that can minimize the mean square error (MSE) of a denoised block in more general cases of noisy nonlocal neighbors. Furthermore, the proposed algorithm searches nonlocal neighbors from an external database as well as the entire input image to improve the performance even when a noisy block may not have similar blocks within the image. Since the extended search range demands a higher computational burden, we develop a probabilistic tree-based search method to reduce the computational complexity. Simulation results show that the proposed algorithm provides significantly better denoising performance than the conventional nonlocal means filter. (C) 2012 Elsevier Inc. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ACADEMIC PRESS INC ELSEVIER SCIENCE | - |
dc.subject | SPARSE | - |
dc.subject | ALGORITHM | - |
dc.subject | DICTIONARIES | - |
dc.title | An MMSE approach to nonlocal image denoising: Theory and practical implementation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Chang-Su | - |
dc.identifier.doi | 10.1016/j.jvcir.2012.01.007 | - |
dc.identifier.scopusid | 2-s2.0-84862816934 | - |
dc.identifier.wosid | 000302208800007 | - |
dc.identifier.bibliographicCitation | JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, v.23, no.3, pp.476 - 490 | - |
dc.relation.isPartOf | JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION | - |
dc.citation.title | JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION | - |
dc.citation.volume | 23 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 476 | - |
dc.citation.endPage | 490 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.subject.keywordPlus | SPARSE | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | DICTIONARIES | - |
dc.subject.keywordAuthor | Image denoising | - |
dc.subject.keywordAuthor | Nonlocal means filter | - |
dc.subject.keywordAuthor | Minimum mean square error (MMSE) denoising | - |
dc.subject.keywordAuthor | Bayesian estimation | - |
dc.subject.keywordAuthor | Noisy nonlocal neighbors | - |
dc.subject.keywordAuthor | Probabilistic tree search | - |
dc.subject.keywordAuthor | External database | - |
dc.subject.keywordAuthor | Image restoration | - |
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