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Title of Journal: Multimed Tools Appl

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Abbravation: Multimedia Tools and Applications

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Springer US

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DOI

10.1002/ejic.200700251

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1573-7721

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Nonlocal sparse regularization model with applica

Authors: Ning He JinBao Wang LuLu Zhang GuangMei Xu Ke Lu
Publish Date: 2015/02/13
Volume: 75, Issue: 5, Pages: 2579-2594
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Abstract

We study problems related to denoising of natural images corrupted by Gaussian white noise Important structures in natural images such as edges and textures are jointly characterized by local variation and nonlocal invariance Both provide valuable schemes in the regularization of image denoising In this paper we propose a framework to explore two sets of ideas involving on the one hand locally learning a dictionary and estimating the sparse regularization signal descriptions for each coefficient and on the other hand nonlocally enforcing the invariance constraint by introducing patch selfsimilarities of natural images into the cost functional The minimization of this new cost functional leads to an iterative thresholdingbased image denoising algorithm its efficient implementation is discussed Experimental results from image denoising tasks of synthetic and real noisy images show that the proposed method outperforms the stateoftheart making it possible to effectively restore raw images from digital cameras at a reasonable speed and memory costThis work was supported by the National Natural Science Foundation of China Grant Nos 61370138 61271435 61103130 U1301251 National Program on Key Basic Research Projects 973 programs Grant Nos 2010CB7318041 2011CB7069014 Project of Construction of Innovative Teams and Teacher Career Development for Universities and Colleges under Beijing Municipality Grant Nos IDHT20130513 CITTCD20130513 Beijing Municipal Natural Science Foundation Grant No 4141003 and Beijing Municipal Party Committee Organization Department of Outstanding Talent Project Grant No 2010D005022000011

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