《计算机应用研究》|Application Research of Computers

一种融合结构张量与非局域全变分的图像去噪方法

Image denoising model based on structure tensor and non-local total variation

免费全文下载 (已被下载 次)  
获取PDF全文
作者 王诗言
机构 重庆邮电大学 通信与信息工程学院,重庆 400065
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2018)03-0930-04
DOI 10.3969/j.issn.1001-3695.2018.03.061
摘要 针对图像去噪中纹理与结构保持,提出了一种融合结构张量与非局域全变分的图像去噪模型。该模型利用非局域思想对图像中的各个像素点进行建模,定义了融合图像非局域信息与结构张量的相似性权重,一方面度量patch间的灰度相似性,能够较好地保持图像的纹理特性;另一方面兼顾图像的几何结构特性,能够调节不同对比度patch之间的权重,尤其是提高了低对比度区域的权重值,同时减少阶梯效应的产生。数值实验表明该方法在去噪的同时很好地保留了图像纹理细节与几何结构信息。
关键词 图像去噪;非局域;全变分;结构张量;分裂Bregman
基金项目 重庆市教委科学技术研究项目(KJ1500426)
重庆市前沿与应用基础研究计划项目(cstc2016jcyjA0542)
重庆邮电大学青年科学基金资助项目(A2014-96)
重庆邮电大学博士启动基金资助项目(A2014-09)
本文URL http://www.arocmag.com/article/01-2018-03-061.html
英文标题 Image denoising model based on structure tensor and non-local total variation
作者英文名 Wang Shiyan
机构英文名 SchoolofCommunication&InformationEngineering,ChongqingUniversityofPosts&Telecommunications,Chongqing400065,China
英文摘要 This paper proposed an image denoising model based on structure tensor and non-local total variation, with regard to texture and structure persevering while denoising the image. This method modeled every pixel with non-local information, defined the similarity weight with both non-local information and structure tensor. The gray similarity between patched through the whole spatial domain was measured to preserve texture features; on the other hand, it took geometric characteristics of the image into account, and adjusted the weight between different contrast patches, especially increasing the weight of the low contrast region and decreasing the staircasing effect in the same time. Numerical experiments validate that the method can effectively denoise the image and preserve texture and geometry information well.
英文关键词 image denoise; non-local; total variation; structure tenor; split Bregman
参考文献 查看稿件参考文献
  [1] Chang S G, Yu Bin, Vetterli M. Adaptive wavelet thresholding for image denoising and compression[J] . IEEE Trans on Image Processing, 2000, 9(9):1532-1546.
[2] Rafael C G, Richard E W. Digital image processing[M] . 2nd ed. Upper Saddle River:Prentice Hall, 2002.
[3] Yaroslavsky L P. Digital picture processing:an introduction[M] . Berlin:Springer, 1985.
[4] Tomasi C, Manduchi R. Bilateral filtering for gray and color images[C] //Proc of International Conference on Computer Vision. 1998:839-846.
[5] Buades A, Coll B, Morel J M. A review of image denoising algorithms with a new one[J] . Multiscale Model Simulation, 2005, 2(4):490-530.
[6] Buades A, Coll B, Morel J M. Image denoising methods a new nonlocal principle[J] . SIAM Review, 2011, 52(1):113-147.
[7] Singer A. Diffusion interpretation of nonlocal neighborhood filter for signal denoising[J] . SIAM Journal on Imaging Sciences, 2009, 2(1):118-139.
[8] Chatterjee P, Milanfar P. A generalization of non-local means via kernel regression[C] // Proc of SPIE Conference on Computational Imaging. 2008.
[9] Dinesh P J, Govindan V K, Mathew A T. Robust estimating approach for nonlocal means based on structurally similar patches[J] . International Journal of Open Problems Compute Science and Mathematics, 2009, 2(2):293-310.
[10] Dong Weisheng, Shi Guangming, Li Xin. Nonlocal image restoration with bilateral variance estimation:a low-rank approach[J] . IEEE Trans on Image Processing, 2013, 22(2):700-711.
[11] Wang Ruxin, Tao Dacheng. Non-local auto-encoder with collaborative stabilization for image restoration[J] . IEEE Trans on Image Processing, 2016, 5(5):2117-2129.
[12] Xie Yuan, Gu Shuhang, Liu Yan, et al. Weighted Schatten p-norm minimization for image denoising and background subtraction[J] . IEEE Trans on Image Processing, 2016, 25(10):4842-4857.
[13] Lu Canyi, Tang Jinhui, Yan Shuicheng, et al. Generalized nonconvex nonsmooth low-rank minimization[C] //Proc of IEEE Computer Vision and Pattern Recognition. 2014:4130-4137.
[14] Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J] . Physica D, 1992, 60(1-4):259-268.
[15] Shu Xianbiao, Ahuja N. Hybrid compressive sampling via a new total variation TVL1[C] //Proc of European Conference on Computer Vision. 2010:393-404.
[16] Chan T, Marquina A T, Muler P. High-order total variation-based image restoration[J] . SIAM Journal on Scientific Computing, 2000, 22(2):503-516.
[17] Bregman L. The relaxation method of finding the common points of convex sets and its application to the solution of problems in convex programming[J] . USSR Computational Mathematics and Mathe-matical Physics, 1967, 7(3):200-217.
[18] Gilboa G, Osher S. Nonlocal operators with applications for image processing, UCLA Report 07-23[R] . 2007.
收稿日期 2016/10/9
修回日期 2017/1/5
页码 930-933,939
中图分类号 TP391
文献标志码 A