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Proceedings Paper

Non-local neighbor embedding image denoising algorithm in sparse domain
Author(s): Guo-chuan Shi; Liang Xia; Shuang-qing Liu; Guo-ming Xu
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Paper Abstract

To get better denoising results, the prior knowledge of nature images should be taken into account to regularize the ill-posed inverse problem. In this paper, we propose an image denoising algorithm via non-local similar neighbor embedding in sparse domain. Firstly, a local statistical feature, namely histograms of oriented gradients of image patches is used to perform the clustering, and then the whole training data set is partitioned into a set of subsets which have similar local geometric structures and the centroid of each subset is also obtained. Secondly, we apply the principal component analysis (PCA) to learn the compact sub-dictionary for each cluster. Next, through sparse coding over the sub-dictionary and neighborhood selecting, the image patch to be synthesized can be approximated by its top k neighbors. The extensive experimental results validate the effective of the proposed method both in PSNR and visual perception.

Paper Details

Date Published: 19 December 2013
PDF: 6 pages
Proc. SPIE 9045, 2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, 90451C (19 December 2013); doi: 10.1117/12.2036660
Show Author Affiliations
Guo-chuan Shi, Hefei New Star Applied Technology Institute (China)
Liang Xia, Hefei New Star Applied Technology Institute (China)
Shuang-qing Liu, Hefei New Star Applied Technology Institute (China)
Guo-ming Xu, Hefei New Star Applied Technology Institute (China)


Published in SPIE Proceedings Vol. 9045:
2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology
Xinggang Lin; Jesse Zheng, Editor(s)

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