REAL-WORLD IMAGE DENOISING VIA WEIGHTED LOW RANK APPROXIMATION

被引:3
作者
Guo, Yuenan [1 ]
Fu, Ying [1 ]
Huang, Hua [1 ]
机构
[1] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing, Peoples R China
来源
2019 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA & EXPO WORKSHOPS (ICMEW) | 2019年
基金
北京市自然科学基金; 中国国家自然科学基金;
关键词
Weighted low rank approximation; alternative direction multiplier method; real-world image denoising; SPARSE REPRESENTATION; ALGORITHM;
D O I
10.1109/ICMEW.2019.00050
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Most of existing denoising algorithms are based on the assumption of additive white Gaussian noise. As the realistic noise in color images captured by CCD or CMOS cameras is much more complex than additive white Gaussian noise, most methods will be not effective. In this paper, we present a weighted low rank approximation for real color image denoising, which effectively models the statistical property of the noise and intrinsic characteristic of the image. Specifically, we employ two weighted matrices to model the realistic noise property along channels and in the spatial dimension in consideration of their different statistics. The intrinsic characteristic of the image is explored via low rank regularization. Then, we formulate the denoising problem into a variational optimization model, which can be solved via the alternating direction method of multipliers (ADMM). Experiments on synthetic and real-world noisy color images show that our proposed method outperforms state-of-the-art denoising methods.
引用
收藏
页码:252 / 257
页数:6
相关论文
共 23 条
[1]  
[Anonymous], 2012, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
[2]   Distributed optimization and statistical learning via the alternating direction method of multipliers [J].
Boyd S. ;
Parikh N. ;
Chu E. ;
Peleato B. ;
Eckstein J. .
Foundations and Trends in Machine Learning, 2010, 3 (01) :1-122
[3]   A non-local algorithm for image denoising [J].
Buades, A ;
Coll, B ;
Morel, JM .
2005 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOL 2, PROCEEDINGS, 2005, :60-65
[4]   A SINGULAR VALUE THRESHOLDING ALGORITHM FOR MATRIX COMPLETION [J].
Cai, Jian-Feng ;
Candes, Emmanuel J. ;
Shen, Zuowei .
SIAM JOURNAL ON OPTIMIZATION, 2010, 20 (04) :1956-1982
[5]   An Efficient Statistical Method for Image Noise Level Estimation [J].
Chen, Guangyong ;
Zhu, Fengyuan ;
Heng, Pheng Ann .
2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2015, :477-485
[6]   Color image denoising via sparse 3d collaborative filtering with grouping constraint in luminance-chrominance space [J].
Dabov, Kostadin ;
Foi, Alessandro ;
Katkovnik, Vladimir ;
Egiazarian, Karen .
2007 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-7, 2007, :313-316
[7]   Image denoising by sparse 3-D transform-domain collaborative filtering [J].
Dabov, Kostadin ;
Foi, Alessandro ;
Katkovnik, Vladimir ;
Egiazarian, Karen .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2007, 16 (08) :2080-2095
[8]   Compressive Sensing via Nonlocal Low-Rank Regularization [J].
Dong, Weisheng ;
Shi, Guangming ;
Li, Xin ;
Ma, Yi ;
Huang, Feng .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2014, 23 (08) :3618-3632
[9]   Nonlocally Centralized Sparse Representation for Image Restoration [J].
Dong, Weisheng ;
Zhang, Lei ;
Shi, Guangming ;
Li, Xin .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2013, 22 (04) :1618-1628
[10]   ON THE DOUGLAS-RACHFORD SPLITTING METHOD AND THE PROXIMAL POINT ALGORITHM FOR MAXIMAL MONOTONE-OPERATORS [J].
ECKSTEIN, J ;
BERTSEKAS, DP .
MATHEMATICAL PROGRAMMING, 1992, 55 (03) :293-318