{"type":"ScholarlyArticle","title":"Multichannel Color Image Denoising via Weighted Schatten p-norm Minimization","authors":[{"name":"Xinjian Huang","slug":"xinjian-huang","given":"Xinjian","family":"Huang","affiliation":"School of Computer Science , Institute of Artificial Intelligence and"},{"name":"Bo Du","slug":"bo-du","given":"Bo","family":"Du","affiliation":"School of Computer Science , Institute of Artificial Intelligence and"},{"name":"Weiwei Liu","slug":"weiwei-liu","given":"Weiwei","family":"Liu","affiliation":"National Engineering Research Center for Multimedia Software , Wuhan University , Wuhan , China"}],"abstract":"The R, G and B channels of a color image generally have different noise statistical properties or noise strengths. It is thus problematic to apply grayscale image denoising algorithms to color image denoising. In this paper, based on the non-local self-similarity of an image and the different noise strength across each channel, we propose a MultiChannel Weighted Schatten p-Norm Minimization (MCWSNM) model for RGB color image denoising. More specifically, considering a small local RGB patch in a noisy image, we first find its nonlocal similar cubic patches in a search window with an appropriate size. These similar cubic patches are then vectorized and grouped to construct a noisy low-rank matrix, which can be recovered using the Schatten p-norm minimization framework. Moreover, a weight matrix is introduced to balance each channel’s contribution to the final denoising results. The proposed MCWSNM can be solved via the alternating direction method of multipliers. Convergence property of the proposed method are also theoretically analyzed . Experiments conducted on both synthetic and real noisy color image datasets demonstrate highly competitive denoising performance, outperforming comparison algorithms, including several methods based on neural networks.","doi":"10.24963/ijcai.2020/89","url":"https://ijcai.org/proceedings/2020/89","pdf":"https://ijcai.org/proceedings/2020/0089.pdf","markdown":"https://ijcai.org/proceedings/2020/89.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/89","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Computer Vision","track":"Main track","number":89,"pages":{"first":637,"last":644},"keywords":["Computer Vision: Other","Computer Vision: 2D and 3D Computer Vision","Machine Learning Applications: Applications of Unsupervised Learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24766/file_24766.m3u8","length_sec":268,"provider":"hls"}],"references":[{"position":1,"raw":"[Buades et al., 2005] Antoni Buades, Bartomeu Coll, and Jean-Michel Morel. A non-local algorithm for image de- noising. In CVPR, pages 60-65, 2005.","title":"A non-local algorithm for image denoising","authors":"Antoni Buades, Bartomeu Coll, Jean-Michel Morel","year":"2005","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chatterjee and Milanfar, 2010] Priyam Chatterjee and Pey- man Milanfar. Is denoising dead? IEEE Transactions on Image Processing, 19(4):895-911, 2010.","title":"Priyam Chatterjee and Peyman Milanfar. Is denoising dead","authors":"Milanfar Chatterjee","year":"2010","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2015] Guangyong Chen, Fengyuan Zhu, and Pheng-Ann Heng. An efficient statistical method for im- age noise level estimation. In ICCV, pages 447-485, 2015.","title":"An efficient statistical method for image noise level estimation","authors":"Chen","year":"2015","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Dabov et al., 2007a] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian. Color image denoising via sparse 3D col- laborative filter with grouping constraint in Luminance- Chrominance space. In ICIP, pages 313-316, 2007.","title":"Color image denoising via sparse 3D collaborative filter with grouping constraint in Luminance-Chrominance space","authors":"Dabov","year":"2007","venue":"ICIP","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Dabov et al., 2007b] Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen O. Egiazarian. Im- age denoising by sparse 3-D transform-domain collabo- rative filtering. IEEE Transactions on Image Processing, 16(8):2080-2095, 2007.","title":"Image denoising by sparse 3-D transform-domain collaborative filtering","authors":"Dabov","year":"2007","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dong et al., 2013a] Weisheng Dong, Guangming Shi, and Xin Li. Nonlocal image restoration with bilateral variance estimation: A low-rank approach. IEEE Transactions on Image Processing, 22(2):700-711, 2013.","title":"Nonlocal image restoration with bilateral variance estimation: A low-rank approach","authors":"Dong","year":"2013","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Dong et al., 2013b] Weisheng Dong, Lei Zhang, Guang- ming Shi, and Xin Li. Nonlocally centralized sparse rep- resentation for image restoration. IEEE Transactions on Image Processing, 22(4):1620-1630, 2013.","title":"Nonlocally centralized sparse representation for image restoration","authors":"Dong","year":"2013","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Fang et al., 2020a] Yixiang Fang, Xin Huang, Lu Qin, Ying Zhang, Wenjie Zhang, Reynold Cheng, and Xuemin Lin. A survey of community search over big graphs. The VLDB Journal, 29:353-392, 2020.","title":"A survey of community search over big graphs","authors":"Fang","year":"2020","venue":"The VLDB Journal","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Fang et al., 2020b] Yixiang Fang, Yixing Yang, Wenjie Zhang, Xuemin Lin, and Xin Gao. Effective and effi- cient community search over large heterogeneous infor- mation networks. Proceedings of the VLDB Endowment, 13(6):854-867, 2020.","title":"Effective and efficient community search over large heterogeneous infor-mation networks","authors":"Fang","year":"2020","venue":"Proceedings of the VLDB Endowment","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Gu et al., 2017] Shuhang Gu, Qi Xie, Deyu Meng, Wang- meng Zuo, Xiangchu Feng, and Lei Zhang. Weighted nuclear norm minimization and its applications to low level vision. International Journal of Computer Vision, 121(2):183-208, 2017.","title":"Weighted nuclear norm minimization and its applications to low level vision","authors":"Shuhang Gu, Qi Xie, Deyu Meng, Wangmeng Zuo, Xiangchu Feng, Lei Zhang","year":"2017","venue":"International Journal of Computer Vision","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hu et al., 2019] Zhentao Hu, Zhiqiang Huang, Xinjian Huang, Fulin Luo, and Renzhen Ye. An adaptive nonlocal gaussian prior for hyperspectral image denoising. IEEE Geoscience and Remote Sensing Letters, 16(9):1487- 1491, 2019.","title":"An adaptive nonlocal gaussian prior for hyperspectral image denoising","authors":"Zhentao Hu, Zhiqiang Huang, Xinjian Huang, Fulin Luo, Renzhen Ye","year":"2019","venue":"IEEE Geoscience and Remote Sensing Letters","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Lebrun et al., 2015] Marc Lebrun, Miguel Colom, and Jean-Michel Morel. The noise clinic: A blind image de- noising algorithm. IPOL Journal, 5:1-54, 2015.","title":"The noise clinic: A blind image denoising algorithm","authors":"Marc Lebrun, Miguel Colom, Jean-Michel Morel","year":"2015","venue":"IPOL Journal","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Liu et al., 2017] Weiwei Liu, Ivor W. Tsang, and Klaus- Robert Müller. An easy-to-hard learning paradigm for multiple classes and multiple labels. Journal of Machine Learning Research, 18(94):1-38, 2017.","title":"An easy-to-hard learning paradigm for multiple classes and multiple labels","authors":"Weiwei Liu, Ivor W Tsang, Klaus-Robert Müller","year":"2017","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu et al., 2018] Ding Liu, BihanWen, Xianming Liu, Zhangyang Wang, and Thomas S. Huang. When image denoising meets high-level vision tasks: A deep learning approach. In IJCAI, pages 842-848, 2018.","title":"When image denoising meets high-level vision tasks: A deep learning approach","authors":"Ding Liu, Xianming Bihanwen, Zhangyang Liu, Thomas S Wang, Huang","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/117"},{"position":15,"raw":"[Liu et al., 2019] Weiwei Liu, Donna Xu, Ivor W. Tsang, and Wenjie Zhang. Metric learning for multi-output tasks. IEEE Transactions on Pattern Analysis and Machine In- telligence, 42(2):408-422, 2019.","title":"Metric learning for multi-output tasks","authors":"Weiwei Liu, Donna Xu, Ivor W Tsang, Wenjie Zhang","year":"2019","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Mairal et al., 2008] Julien Mairal, Michael Elad, and Guillermo Sapiro. Sparse representation for color im- age restoration. IEEE Transactions on Image Processing, 17(1):53-69, 2008.","title":"Sparse representation for color image restoration","authors":"Julien Mairal, Michael Elad, Guillermo Sapiro","year":"2008","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Mairal et al., 2012] Julien Mairal, Francis R. Bach, and Jean Ponce. Task-driven dictionary learning. IEEE Trans- actions on Pattern Analysis and Machine Intelligence, 34(4):791-804, 2012.","title":"Task-driven dictionary learning","authors":"Julien Mairal, Francis R Bach, Jean Ponce","year":"2012","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Marsousi et al., 2014] Mahdi Marsousi, Kaveh Abhari, Paul Babyn, and Javad Alirezaie. An adaptive approach to learn overcomplete dictionaries with efficient numbers of elements. IEEE Transactions on Signal Processing, 62(12):3272-3283, 2014.","title":"An adaptive approach to learn overcomplete dictionaries with efficient numbers of elements","authors":"Marsousi","year":"2014","venue":"IEEE Transactions on Signal Processing","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Nam et al., 2016] Seonghyeon Nam, Youngbae Hwang, Ya- suyuki Matsushita, and Seon Joo Kim. A holistic approach to cross-channel image noise modeling and its application to image denoising. In CVPR, pages 1683-1691, 2016.","title":"A holistic approach to cross-channel image noise modeling and its application to image denoising","authors":"Nam","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Roth and Black, 2005] Stefan Roth and Michael J. Black. Fields of experts: A framework for learning image priors. In CVPR, volume 2, pages 860-867, 2005.","title":"Fields of experts: A framework for learning image priors","authors":"Stefan Roth, Michael J Black","year":"2005","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Starck et al., 2002] Jean-Luc Starck, Emmanuel J. Candès, and David L. Donoho. The curvelet transform for im- age denoising. IEEE Transactions on Image Processing, 11(6):670-684, 2002.","title":"The curvelet transform for image denoising","authors":"Jean-Luc Starck, Emmanuel J Candès, David L Donoho","year":"2002","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wang et al., 2018a] Zengmao Wang, Yuhong Guo, and Bo Du. Matrix completion with preference ranking for top-N recommendation. In IJCAI, pages 3585-3591, 2018.","title":"Matrix completion with preference ranking for top-N recommendation","authors":"Wang","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/498"},{"position":23,"raw":"[Wang et al., 2018b] Zheng Wang, Xiang Bai, Mang Ye, and Shin'ichi Satoh. Incremental deep hidden attribute learn- ing. In ACM MM, pages 72-80, 2018.","title":"Incremental deep hidden attribute learning","authors":"Wang","year":"2018","venue":"ACM MM","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Xie et al., 2016] Yuan Xie, Shuhang Gu, Yan Liu, Wang- meng Zuo, Wensheng Zhang, and Lei Zhang. Weight- ed Schatten p-norm minimization for image denoising and background subtraction. IEEE Transactions on Image Pro- cessing, 25(10):4842-4857, 2016.","title":"Weighted Schatten p-norm minimization for image denoising and background subtraction","authors":"Yuan Xie, Shuhang Gu, Yan Liu, Wangmeng Zuo, Wensheng Zhang, Lei Zhang","year":"2016","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Xu et al., 2015] Jun Xu, Lei Zhang, Wangmeng Zuo, David Zhang, and Xiangchu Feng. Patch group based nonlocal self-similarity prior learning for image denoising. In IC- CV, pages 244-252, 2015.","title":"Patch group based nonlocal self-similarity prior learning for image denoising","authors":"Jun Xu, Lei Zhang, Wangmeng Zuo, David Zhang, Xiangchu Feng","year":"2015","venue":"IC-CV","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Xu et al., 2017a] Chen Xu, Zhouchen Lin, and Hongbin Zha. A unified convex surrogate for the Schatten-p nor- m. In AAAI, pages 926-932, 2017.","title":"A unified convex surrogate for the Schatten-p norm","authors":"Xu","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Xu et al., 2017b] Jun Xu, Lei Zhang, David Zhang, and Xi- angchu Feng. Multi-channel weighted nuclear norm min- imization for real color image denoising. In ICCV, pages 1096-1104, 2017.","title":"Multi-channel weighted nuclear norm minimization for real color image denoising","authors":"Xu","year":"2017","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Ye et al., 2018] Mang Ye, Zheng Wang, Xiangyuan Lan, and Pong C. Yuen. Visible thermal person re-identification via dual-constrained top-ranking. In IJCAI, pages 1092- 1099, 2018.","title":"Visible thermal person re-identification via dual-constrained top-ranking","authors":"Mang Ye, Zheng Wang, Xiangyuan Lan, Pong C Yuen","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/152"},{"position":29,"raw":"[Zhang and Aeron, 2016] Zemin Zhang and Shuchin Aeron. Denoising and completion of 3D data via multidimension- al dictionary learning. In IJCAI, pages 2371-2377, 2016.","title":"Denoising and completion of 3D data via multidimensional dictionary learning","authors":"Aeron Zhang, Zemin Zhang, Shuchin Aeron","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/338"},{"position":30,"raw":"[Zhang et al., 2013] Min Zhang, Zheng-Hai Huang, and Y- ing Zhang. Restricted p-isometry properties of nonconvex matrix recovery. IEEE Transactions on Information Theo- ry, 59(7):4316-4323, 2013.","title":"Restricted p-isometry properties of nonconvex matrix recovery","authors":"Zhang","year":"2013","venue":"IEEE Transactions on Information Theory","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Zhang et al., 2017a] Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. Beyond a Gaussian de- noiser: Residual learning of deep CNN for image denois- ing. IEEE Transactions on Image Processing, 26(7):3142- 3155, 2017.","title":"Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising","authors":"Zhang","year":"2017","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Zhang et al., 2017b] Kai Zhang, Wangmeng Zuo, Shuhang Gu, and Lei Zhang. Learning deep CNN denoiser prior for image restoration. In CVPR, pages 2808-2817, 2017.","title":"Learning deep CNN denoiser prior for image restoration","authors":"Zhang","year":"2017","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Zhang et al., 2018] Kai Zhang, Wangmeng Zuo, and Lei Zhang. FFDNet: Toward a fast and flexible solution for CNN-based image denoising. IEEE Transactions on Im- age Processing, 27(9):4608-4622, 2018.","title":"FFDNet: Toward a fast and flexible solution for CNN-based image denoising","authors":"Kai Zhang, Wangmeng Zuo, Lei Zhang","year":"2018","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Zhu et al., 2016] Fengyuan Zhu, Guangyong Chen, and Pheng-Ann Heng. From noise modeling to blind image denoising. In CVPR, pages 420-429, 2016.","title":"From noise modeling to blind image denoising","authors":"Fengyuan Zhu, Guangyong Chen, Pheng-Ann Heng","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}