计算机科学 ›› 2016, Vol. 43 ›› Issue (1): 298-301.doi: 10.11896/j.issn.1002-137X.2016.01.064
王圳萍,张家树,陈高
WANG Zhen-ping, ZHANG Jia-shu and CHEN Gao
摘要: 传统的基于低秩矩阵恢复的图像去噪算法只对低秩部分进行约束,当高斯噪声过大时,会导致去噪不充分或细节严重丢失。针对此问题,提出了一种新的鲁棒的图像去噪模型。该模型在原有的低秩矩阵核范数约束的基础上引入高斯噪声约束项,此外为了提高低秩矩阵的低秩性和稀疏矩阵的稀疏性,引入了加权的方法。为了考察方法的去噪能力,选取了不同参数类型的混合噪声图像进行仿真,并结合峰值信噪比、结构相似度评价标准与传统的基于低秩矩阵恢复的图像去噪算法进行对比。实验结果表明,加权低秩矩阵恢复的混合噪声图像去噪算法能增加低秩矩阵的低秩性和稀疏矩阵的稀疏性,在保证去噪效果的同时,保留了图像的细节信息,具有更佳的视觉效果,同时,客观评价指标均有所提高。
[1] Chatterjee P,Milanfar P.Is denoising dead?[J].IEEE Transactions on Image Processing,2010,19(4):895-911 [2] Buades A,Coll B,Morel J M.A review of image denoising algorithms,with a new one[J].Multiscale Modeling & Simulation,2005,4(2):490-530 [3] Kervrann C,Boulanger J.Optimal spatial adaptation for patch-based image denoising[J].IEEE Transactions on Image Proces-sing,2006,15(10):2866-2878 [4] Candès E J,Li X,Ma Y,et al.Robust principal component analy-sis?[J].Journal of the ACM (JACM),2011,58(3):11 [5] Wright J,Yang A Y,Ganesh A,et al.Robust face recognition via sparse representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-227 [6] Peng Y,Ganesh A,Wright J,et al.RASL:Robust alignment by sparse and low-rank decomposition for linearly correlated images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2233-2246 [7] Zhang Z,Ganesh A,Liang X,et al.TILT:transform invariant low-rank textures[J].International Journal of Computer Vision,2012,99(1):1-24 [8] Ji H,Liu C,Shen Z,et al.Robust video denoising using low rank matrix completion[C]∥2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE,2010:1791-1798 [9] LIU Xin-yan,MA Jie,ZHANG Xiao-mei,et al.Image denoising of low-rank matrix recovery via joint Frobenius norm [J].Journal of Image and Graphics,2014,19(4):502-511 [10] Wright J,Ganesh A,Rao S,et al.Robust principal component analysis:Exact recovery of corrupted low-rank matrices via convex optimization[C]∥Advances in Neural Information Proces-sing Systems.2009:2080-2088 [11] Ganesh A,Wright J,Li X,et al.Dense error correction for low-rank matrices via principal component pursuit[C]∥2010 IEEE International Symposium on Information Theory Proceedings (ISIT).IEEE,2010:1513-1517 [12] Zheng Xiu-qing,He Kun,Zhang Jian.Image denoising by principal component analysis with structural information [J].Computer Science,2014,41(8):301-305(in Chinese) 郑秀清,何坤,张健.基于结构信息的RPAC图像去噪[J].计算机科学,2014,41(8):301-305 [13] Candes E J.The restricted isometry property and its implications for compressed sensing[J].Comptes Rendus Mathematique,2008,346(9):589-592 [14] Lopez-Rubio E.Restoration of images corrupted by Gaussianand uniform impulsive noise[J].Pattern Recognition,2010,43(5):1835-1846 [15] Candes E J,Wakin M B,Boyd S P.Enhancing sparsity by reweighted l1 minimization[J].Journal of Fourier Analysis and Applications,2008,14(5/6):877-905 |
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