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Combining Total Variation and Nonlocal Means Regularization for Edge Preserving Image Deconvolution
Hao, Binbin (China University of Petroleum)
Zhu, Jianguang (Shandong University of Science and Technology)

Date: 2011
Abstract: We propose a new edge preserving image deconvolution model by combining total variation and nonlocal means regularization. Natural images exhibit an high degree of redundancy. Using this redundancy, the nonlocal means regularization strategy is a good technique for detail preserving image restoration. In order to further improve the visual quality of the nonlocal means based algorithm, total variation is introduced to the model to better preserve edges. Then an efficient alternating minimization procedure is used to solve the model. Numerical experiments illustrate the effectiveness of the proposed algorithm.
Rights: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Language: Anglès
Document: article ; recerca ; publishedVersion
Subject: Total Variation Regularization ; Nonlocal Means Filter ; Image Deconvolution
Published in: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 10, Núm. 1 (December 2011) , p. 42-53, ISSN 1577-5097

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DOI: 10.5565/rev/elcvia.373

11 p, 760.9 KB

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Articles > Published articles > ELCVIA : Electronic Letters on Computer Vision and Image Analysis
Articles > Research articles

 Record created 2012-02-15, last modified 2017-02-07

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