Computer Science ›› 2017, Vol. 44 ›› Issue (5): 299-303.doi: 10.11896/j.issn.1002-137X.2017.05.055

Previous Articles     Next Articles

Image Denoising Method of Spectrum Clustering Based on Non-local Similarity

KE Zu-fu, YI Ben-shun and XIE Qiu-ying   

  • Online:2018-11-13 Published:2018-11-13

Abstract: The conventional image denoising algorithms just make use of the prior information of the natural image or the noise image alone,without effective combination of the prior imformation of two images to realize the image denoi-sing.For this problem,a novel image denosing method which joins the prior information of the natural image and the non-local similarity of the noise image was proposed in this paper.Firstly,the similar blocks in natural image are clustered in the same class by the spectrum clustering,and the result of the spectrum clustering with the natural image is used to get the clustering of the noise image blocks.Then,the gotten same class blocks of the noise image are vectorized as a low-rank matrix.Secondly,the low-rank approximation process is adopted on the matrix to estimate the relative original image data.Finally,the original image can be reconstructed by the estimated image data.The experimental results show that compared with the RNL(adaptive regularization of the NL-Means) and LPG-PCA(two-stage image denoising by principal component analysis with local pixel grouping),the proposed algorithm can provide significant performance improvement with respect to both PSNR and local information preservation,which produces better denoising effect.

Key words: Image denoising,Spectrum clustering,Non-local self-similarity,Low-rank approximation

[1] WANG Z P,ZHANG J S,CHEN G.Mixture noise image denoising using reweighted low-rank matrix recovery[J].Compu-ter Science,2016,43(1):298-301.(in Chinese) 王圳萍,张家树,陈高.加权低秩矩阵恢复的混合噪声图像去噪[J].计算机科学,2016,3(1):298-301.
[2] HUANG D A,KANG L W,WANG Y C,et al.Self-learning based image decomposition with applications to single image denoising[J].IEEE Transactions on Multimedia,2014,16(1):83-93.
[3] XIE K,ZHANG F.Overcomplete representation base image denoising algorithm[J].Chinese Journal of Electronics,2013,41(10):1911-1916.(in Chinese) 解凯,张芬.基于过完备表示的图像去噪算法[J].电子学报,2013,41(10):1911-1916.
[4] GUO Q,ZHANG C,ZHANG Y,et al.An efficient svd-basedmethod for image denoising[J].IEEE Transactions on Circuits and Systems for Video Technology,2016,26(6):868-880.
[5] RUDIN L,OSHER S,FATEMI E.Nonlinear total variationbased noise removal algorithms[J].Phys.D,1992,60(1):259-268.
[6] GILBOA G,SOCHEN N,ZEEVI Y Y.Variational denoising of partly textured images by spatially varying constraints[J].IEEE Trans.Image Process,2006,15(8):2281-2289.
[7] BUADES A,COLL B,MOREL J M.A non-local algorithm for image denoising[C]∥Proc.CVPR.2005.
[8] SUTOUR C,DELEDALLE C A,AUJOL J F.Adaptive regulari-zation of the NL-Means:application to image and video denoi-sing[J].IEEE Transactions on Image Processing,2014,23(8):3506-3521.
[9] MAHMOUDI M,SAPIRO G.Fast image and video denoisingvia non-local means of similar neighborhoods[J].IEEE Signal Processing Letters,2005,12(12):839-842
[10] ZHANG X D,FENG X C,Wang W W.Two-direction nonlocal model for image denoising[J].IEEE Transactions on Image Processing,2013,22(1):408-412.
[11] XIAO J S,LI W H,JIANG H,et al.Three dimensional block-matching video denoising algorithm based on dual-domain filtering[J].Journal on Communications,2015,36(9):91-97.(in Chinese) 肖进胜,李文昊,姜红,等.基于双域滤波的三维块匹配视频去噪算法[J].通信学报,2015,6(9):91-97.
[12] DABOV K,FOI A,KATKOVNIK V,et al.Image denoising by sparse 3-d transform-domain collaborative filtering[J].IEEE Trans.on Image Process,2007,16(8):2080-2095.
[13] ZHANG L,DONG W S,ZHANG D,et al.Two-stage image denoisng by principal component analysis with local pixel grouping[J].Pattern Recognition,2010,43(4):1531-1549.
[14] HE Y M,GAN T,CHEN W F,et al.Adaptive denoising by singular value decomposition[J].IEEE Signal Processing Letters,2011,18(4):215-219.
[15] GU S,ZHANG L,ZUO W,et al.Weighted nuclear norm minimization with application to image denoising[C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition.Columbus,OH,2014:2862-2869.
[16] JIN J G.Review of clustering method[J].Computer Science,2014,41(11A):288-293.(in Chinese) 金建国.聚类方法综述[J].计算机科学,2014,1(11A):288-293.
[17] LUXBURG U V.A tutorial on spectral clustering[J].Statistics & Computing,2007,17(17):395-416.
[18] CHUNG F.Spectral Graph Theory[M].Am.Math.Soc,1997.
[19] ZHENG Q,LIU Z.Research on improved normalized cut spectral clustering algorithm[C]∥2016 Chinese Control and Decision Conference (CCDC).Yinchuan,2016:1981-1984.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!