{"type":"ScholarlyArticle","title":"Degradation Accordant Plug-and-Play for Low-Rank Tensor Completion","authors":[{"name":"Yexun Hu","slug":"yexun-hu","given":"Yexun","family":"Hu","affiliation":"School of Computing and Artificial Intelligence , Southwestern University of Finance and Economics"},{"name":"Tai-Xiang Jiang","slug":"tai-xiang-jiang","given":"Tai-Xiang","family":"Jiang","affiliation":"School of Computing and Artificial Intelligence , Southwestern University of Finance and Economics"},{"name":"Xi-Le Zhao","slug":"xi-le-zhao","given":"Xi-Le","family":"Zhao","affiliation":"School of Mathematical Sciences , University of Electronic Science and Technology of China"}],"abstract":"Tensor completion aims at estimating missing values from an incomplete observation, playing a fundamental role for many applications. This work proposes a novel low-rank tensor completion model, in which the inherent low-rank prior and external degradation accordant data-driven prior are simultaneously utilized. Specifically, the tensor nuclear norm (TNN) is adopted to characterize the overall low-dimensionality of the tensor data. Meanwhile, an implicit regularizer is formulated and its related subproblem is solved via a deep convolutional neural network (CNN) under the plug-and-play framework. This CNN, pretrained for the inpainting task on a mass of natural images, is expected to express the external data-driven prior and this plugged inpainter is consistent with the original degradation process. Then, an efficient alternating direction method of multipliers (ADMM) is designed to solve the proposed optimization model. Extensive experiments are conducted on different types of tensor imaging data with the comparison with state-of-the-art methods,  illustrating the effectiveness and the remarkable generalization ability of our method.","doi":"10.24963/ijcai.2022/256","url":"https://ijcai.org/proceedings/2022/256","pdf":"https://ijcai.org/proceedings/2022/0256.pdf","markdown":"https://ijcai.org/proceedings/2022/256.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/256","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Constraint Satisfaction and Optimization","track":"Main Track","number":256,"pages":{"first":1843,"last":1849},"keywords":["Computer Vision: Machine Learning for Vision","Computer Vision: Computational photography","Constraint Satisfaction and Optimization: Constraints and Machine Learning","Constraint Satisfaction and Optimization: Modeling"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986561","length_sec":86,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984704","length_sec":344,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Boyd et al., 2011] Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, and Jonathan Eckstein. Distributed opti- mization and statistical learning via the alternating direc- tion method of multipliers. Foundations and Trends® in Machine Learning, 3(1):1-122, 2011.","title":"Distributed optimization and statistical learning via the alternating direction method of multipliers","authors":"Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein","year":"2011","venue":"Foundations and Trends® in Machine Learning","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Dabov et al., 2007] Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian. Image denoising by sparse 3-D transform-domain collabora- tive filtering. IEEE Transactions on image processing, 16(8):2080-2095, 2007.","title":"Image denoising by sparse 3-D transform-domain collaborative filtering","authors":"Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, Karen Egiazarian","year":"2007","venue":"IEEE Transactions on image processing","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Jiang et al., 2018] Fei Jiang, Xiao-Yang Liu, Hongtao Lu, and Ruimin Shen. Anisotropic total variation regularized low-rank tensor completion based on tensor nuclear norm for color image inpainting. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pages 1363-1367, 2018.","title":"Anisotropic total variation regularized low-rank tensor completion based on tensor nuclear norm for color image inpainting","authors":"Fei Jiang, Xiao-Yang Liu, Hongtao Lu, Ruimin Shen","year":"2018","venue":"Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Jiang et al., 2020] Tai-Xiang Jiang, Michael K Ng, Xi-Le Zhao, and Ting-Zhu Huang. Framelet representation of tensor nuclear norm for third-order tensor completion. IEEE Transactions on Image Processing, 29:7233-7244, 2020.","title":"Framelet representation of tensor nuclear norm for third-order tensor completion","authors":"Tai-Xiang Jiang, Michael K Ng, Xi-Le Zhao, Ting-Zhu Huang","year":"2020","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Kilmer and Martin, 2011] Misha E Kilmer and Carla D Martin. Factorization strategies for third-order tensors. Linear Algebra and its Applications, 435(3):641-658, 2011.","title":"Factorization strategies for third-order tensors","authors":"Misha E Kilmer, Carla D Martin","year":"2011","venue":"Linear Algebra and its Applications","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Kilmer et al., 2013] Misha E Kilmer, Karen Braman, Ning Hao, and Randy C Hoover. Third-order tensors as opera- tors on matrices: A theoretical and computational frame- work with applications in imaging. SIAM Journal on Ma- trix Analysis and Applications, 34(1):148-172, 2013.","title":"Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging","authors":"Misha E Kilmer, Karen Braman, Ning Hao, Randy C Hoover","year":"2013","venue":"SIAM Journal on Matrix Analysis and Applications","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Kolda et al., 2005] Tamara G Kolda, Brett W Bader, and Joseph P Kenny. Higher-order web link analysis using multilinear algebra. In Proceedings of the IEEE Interna- tional Conference on Data Mining, pages 242-249, 2005.","title":"Higher-order web link analysis using multilinear algebra","authors":"Tamara G Kolda, Brett W Bader, Joseph P Kenny","year":"2005","venue":"Proceedings of the IEEE International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Liu et al., 2013] Ji Liu, Przemyslaw Musialski, Peter Wonka, and Jieping Ye. Tensor completion for estimating missing values in visual data. IEEE Transactions on Pat- tern Analysis and Machine Intelligence, 35(1):208-220, 2013.","title":"Tensor completion for estimating missing values in visual data","authors":"Ji Liu, Przemyslaw Musialski, Peter Wonka, Jieping Ye","year":"2013","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Liu et al., 2015] Xiao-Yang Liu, Shuchin Aeron, Vaneet Aggarwal, Xiaodong Wang, and Min-You Wu. Adap- tive sampling of RF fingerprints for fine-grained indoor localization. IEEE Transactions on Mobile Computing, 15(10):2411-2423, 2015.","title":"Adaptive sampling of RF fingerprints for fine-grained indoor localization","authors":"Xiao-Yang Liu, Shuchin Aeron, Vaneet Aggarwal, Xiaodong Wang, Min-You Wu","year":"2015","venue":"IEEE Transactions on Mobile Computing","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Lu et al., 2019] Canyi Lu, Xi Peng, and Yunchao Wei. Low- rank tensor completion with a new tensor nuclear norm induced by invertible linear transforms. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5996-6004, 2019.","title":"Lowrank tensor completion with a new tensor nuclear norm induced by invertible linear transforms","authors":"Lu","year":"2019","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Nikolova and Ng, 2005] Mila Nikolova and Michael K Ng. Analysis of half-quadratic minimization methods for sig- nal and image recovery. SIAM Journal on Scientific com- puting, 27(3):937-966, 2005.","title":"Analysis of half-quadratic minimization methods for signal and image recovery","authors":"Nikolova, ; Ng, Mila Nikolova, Michael K Ng","year":"2005","venue":"SIAM Journal on Scientific computing","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Oh et al., 2019] Seoung Wug Oh, Sungho Lee, Joon-Young Lee, and Seon Joo Kim. Onion-peel networks for deep video completion. In Proceedings of the IEEE/CVF In- ternational Conference on Computer Vision, pages 4403- 4412, 2019.","title":"Onion-peel networks for deep video completion","authors":"Oh","year":"2019","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Ryu et al., 2019] Ernest Ryu, Jialin Liu, Sicheng Wang, Xi- aohan Chen, Zhangyang Wang, and Wotao Yin. Plug-and- play methods provably converge with properly trained de- noisers. In Proceedings of the International Conference on Machine Learning, pages 5546-5557, 2019.","title":"Plug-andplay methods provably converge with properly trained denoisers","authors":"Ernest Ryu, Jialin Liu, Sicheng Wang, Xiaohan Chen, Zhangyang Wang, Wotao Yin","year":"2019","venue":"Proceedings of the International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Sun et al., 2005] Jian-Tao Sun, Hua-Jun Zeng, Huan Liu, Yuchang Lu, and Zheng Chen. CubeSVD: a novel ap- proach to personalized web search. In Proceedings of the International Conference on World Wide Web, pages 382- 390, 2005.","title":"CubeSVD: a novel approach to personalized web search","authors":"Jian-Tao Sun, Hua-Jun Zeng, Huan Liu, Yuchang Lu, Zheng Chen","year":"2005","venue":"Proceedings of the International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Venkatakrishnan et al., 2013] Singanallur V Venkatakrish- nan, Charles A Bouman, and Brendt Wohlberg. Plug-and- play priors for model based reconstruction. In Proceedings of the IEEE Global Conference on Signal and Information Processing, pages 945-948, 2013.","title":"Plug-andplay priors for model based reconstruction","authors":"Charles A Singanallur V Venkatakrishnan, Brendt Bouman, Wohlberg","year":"2013","venue":"Proceedings of the IEEE Global Conference on Signal and Information Processing","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Wang et al., 2004] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Trans- actions on Image Processing, 13(4):600-612, 2004.","title":"Image quality assessment: from error visibility to structural similarity","authors":"Zhou Wang, Alan C Bovik, Hamid R Sheikh, Eero P Simoncelli","year":"2004","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Yu et al., 2019] Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S Huang. Free-form image inpainting with gated convolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4471-4480, 2019.","title":"Free-form image inpainting with gated convolution","authors":"Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, Thomas S Huang","year":"2019","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Zhang and Aeron, 2017] Zemin Zhang and Shuchin Aeron. Exact tensor completion using t-SVD. IEEE Transactions on Signal Processing, 65(6):1511-1526, 2017.","title":"Exact tensor completion using t-SVD","authors":"Aeron Zhang, Zemin Zhang, Shuchin Aeron","year":"2017","venue":"IEEE Transactions on Signal Processing","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Zhang et al., 2021] Kai Zhang, Yawei Li, Wangmeng Zuo, Lei Zhang, Luc Van Gool, and Radu Timofte. Plug-and- play image restoration with deep denoiser prior. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 2021.","title":"Plug-andplay image restoration with deep denoiser prior","authors":"Kai Zhang, Yawei Li, Wangmeng Zuo, Lei Zhang, Luc Van Gool, Radu Timofte","year":"2021","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Zhao et al., 2020] Xi-Le Zhao, Wen-Hao Xu, Tai-Xiang Jiang, Yao Wang, and Michael K Ng. Deep plug-and-play prior for low-rank tensor completion. Neurocomputing, 400:137-149, 2020.","title":"Deep plug-and-play prior for low-rank tensor completion","authors":"Xi-Le Zhao, Wen-Hao Xu, Tai-Xiang Jiang, Yao Wang, Michael K Ng","year":"2020","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}