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Structure-Based Low-Rank Model With Graph Nuclear Norm Regularization for Noise Removal
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2017 (engelsk)Inngår i: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 26, nr 7, s. 3098-3112Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Nonlocal image representation methods, including group-based sparse coding and block-matching 3-D filtering, have shown their great performance in application to low-level tasks. The nonlocal prior is extracted from each group consisting of patches with similar intensities. Grouping patches based on intensity similarity, however, gives rise to disturbance and inaccuracy in estimation of the true images. To address this problem, we propose a structure-based low-rank model with graph nuclear norm regularization. We exploit the local manifold structure inside a patch and group the patches by the distance metric of manifold structure. With the manifold structure information, a graph nuclear norm regularization is established and incorporated into a low-rank approximation model. We then prove that the graph-based regularization is equivalent to a weighted nuclear norm and the proposed model can be solved by a weighted singular-value thresholding algorithm. Extensive experiments on additive white Gaussian noise removal and mixed noise removal demonstrate that the proposed method achieves a better performance than several state-of-the-art algorithms.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2017. Vol. 26, nr 7, s. 3098-3112
Emneord [en]
Low-rank model, graph nuclear norm regularization, manifold structure, mixed noise removal, weighted singular-value thresholding algorithm
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-208781DOI: 10.1109/TIP.2016.2639781ISI: 000401297400002PubMedID: 28113320Scopus ID: 2-s2.0-85021742176OAI: oai:DiVA.org:kth-208781DiVA, id: diva2:1109649
Merknad

QC 20170614

Tilgjengelig fra: 2017-06-14 Laget: 2017-06-14 Sist oppdatert: 2018-09-19bibliografisk kontrollert

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Li, Hai-Bo
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