{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T12:10:03Z","timestamp":1732536603130,"version":"3.28.0"},"reference-count":50,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2024,8,1]]},"DOI":"10.1587\/transinf.2023edp7265","type":"journal-article","created":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T22:17:43Z","timestamp":1722464263000},"page":"992-1006","source":"Crossref","is-referenced-by-count":0,"title":["Nuclear Norm Minus Frobenius Norm Minimization with Rank Residual Constraint for Image Denoising"],"prefix":"10.1587","volume":"E107.D","author":[{"given":"Hua","family":"HUANG","sequence":"first","affiliation":[{"name":"Information Construction and Management Center, Chongqing Vocational Institute of Engineering"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiwen","family":"SHAN","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Southwest University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuan","family":"LI","sequence":"additional","affiliation":[{"name":"Big Data and Intelligence Engineering School, Chongqing College of International Business and Economics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi","family":"WANG","sequence":"additional","affiliation":[{"name":"Big Data and Intelligence Engineering School, Chongqing College of International Business and Economics"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] Z. Liu, D. Hu, Z. Wang, J. Gou, and T. Jia, \u201cLatLRR for subspace clustering via reweighted Frobenius norm minimization,\u201d Expert Syst. Appl., vol.224, Art. no.119977, Aug. 2023. 10.1016\/j.eswa.2023.119977","DOI":"10.1016\/j.eswa.2023.119977"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] Y. Chang, L. Yan, T. Wu, and S. Zhong, \u201cRemote sensing image stripe noise removal: From image decomposition perspective,\u201d IEEE Trans. Geosci. Remote Sens., vol.54, no.12, pp.7018-7031, 2016. 10.1109\/tgrs.2016.2594080","DOI":"10.1109\/TGRS.2016.2594080"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] G. Bi, G. Si, Y. Zhao, B. Qi, and H. Lv, \u201cHaze removal for a single remote sensing image using low-rank and sparse prior,\u201d IEEE Trans. Geosci. Remote Sensing, vol.60, pp.1-13, Dec. 2022. 10.1109\/tgrs.2021.3135975","DOI":"10.1109\/TGRS.2021.3135975"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] X. Ding, C. Shen, T. Zeng, and Y. Peng, \u201cSAB Net: A semantic attention boosting framework for semantic segmentation,\u201d IEEE Trans. Neural Netw. Learn. Syst., pp.1-13, 2022. 10.1109\/tnnls.2022.3144003","DOI":"10.1109\/TNNLS.2022.3144003"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] P.-H. Hsiao, F.-J. Chang, and Y.-Y. Lin, \u201cLearning discriminatively reconstructed source data for object recognition with few examples,\u201d IEEE Trans. Image Process., vol.25, no.8, pp.3518-3532, 2016. 10.1109\/tip.2016.2572602","DOI":"10.1109\/TIP.2016.2572602"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] X. Jiang and J. Lai, \u201cSparse and dense hybrid representation via dictionary decomposition for face recognition,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.37, no.5, pp.1067-1079, May 2015. 10.1109\/tpami.2014.2359453","DOI":"10.1109\/TPAMI.2014.2359453"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] S. Liao, A.K. Jain, and S.Z. Li, \u201cPartial face recognition: Alignment-free approach,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.35, no.5, pp.1193-1205, May 2013. 10.1109\/tpami.2012.191","DOI":"10.1109\/TPAMI.2012.191"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] S. Shen, \u201cAccurate multiple view 3D reconstruction using patch-based stereo for large-scale scenes,\u201d IEEE Trans. Image Process., vol.22, no.5, pp.1901-1914, May 2013. 10.1109\/tip.2013.2237921","DOI":"10.1109\/TIP.2013.2237921"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] Z. Wang, Y. Liu, X. Luo, J. Wang, C. Gao, D. Peng, and W. Chen, \u201cLarge-scale affine matrix rank minimization with a novel nonconvex regularizer,\u201d IEEE Trans. Neural Netw. Learn. Syst., vol.33, no.9, pp.4661-4675, Sept. 2022. 10.1109\/tnnls.2021.3059711","DOI":"10.1109\/TNNLS.2021.3059711"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] Z. Wang, W. Wang, J. Wang, and S. Chen, \u201cFast and efficient algorithm for matrix completion via closed-form 2\/3-thresholding operator,\u201d Neurocomputing, vol.330, pp.212-222, Feb. 2019. 10.1016\/j.neucom.2018.10.065","DOI":"10.1016\/j.neucom.2018.10.065"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] Z. Wang, D. Hu, X. Luo, W. Wang, J. Wang, and W. Chen, \u201cPerformance guarantees of transformed Schatten-1 regularization for exact low-rank matrix recovery,\u201d Int. J. Mach. Learn. Cybern., vol.12, pp.3379-3395, June 2021. 10.1007\/s13042-021-01361-1","DOI":"10.1007\/s13042-021-01361-1"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] Z. Wang, C. Gao, X. Luo, M. Tang, J. Wang, and W. Chen, \u201cAccelerated inexact matrix completion algorithm via closed-form q-thresholding (<i>q<\/i>=1\/2, 2\/3) operator,\u201d Int. J. Mach. Learn. Cybern., vol.11, pp.2327-2339, April 2020. 10.1007\/s13042-020-01121-7","DOI":"10.1007\/s13042-020-01121-7"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] Y. Shan, D. Hu, Z. Wang, and T. Jia, \u201cMulti-channel nuclear norm minus Frobenius norm minimization for color image denoising,\u201d Signal Process., vol.207, Art. no.108959, June 2023. 10.1016\/j.sigpro.2023.108959","DOI":"10.1016\/j.sigpro.2023.108959"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] S. Gu, Q. Xie, D. Meng, W. Zuo, X. Feng, and L. Zhang, \u201cWeighted nuclear norm minimization and its applications to low level vision,\u201d Int. J. Comput. Vis., vol.121, pp.183-208, July 2017. 10.1007\/s11263-016-0930-5","DOI":"10.1007\/s11263-016-0930-5"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] Y. Xie, S. Gu, Y. Liu, W. Zuo, W. Zhang, and L. Zhang, \u201cWeighted Schatten <i>p<\/i>-norm minimization for image denoising and background subtraction,\u201d IEEE Trans. Image Process., vol.25, no.10, pp.4842-4857, Aug. 2016. 10.1109\/tip.2016.2599290","DOI":"10.1109\/TIP.2016.2599290"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, \u201cImage denoising by sparse 3-D transform-domain collaborative filtering,\u201d IEEE Trans. Image Process., vol.16, no.8, pp.2080-2095, July 2007. 10.1109\/tip.2007.901238","DOI":"10.1109\/TIP.2007.901238"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] M. Elad and M. Aharon, \u201cImage denoising via sparse and redundant representations over learned dictionaries,\u201d IEEE Trans. Image Process., vol.15, no.12, pp.3736-3745, 2006. 10.1109\/tip.2006.881969","DOI":"10.1109\/TIP.2006.881969"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] Z. Zha, X. Yuan, B. Wen, J. Zhou, and C. Zhu, \u201cGroup sparsity residual constraint with non-local priors for image restoration,\u201d IEEE Trans. Image Process., vol.29, pp.8960-8975, 2020. 10.1109\/tip.2020.3021291","DOI":"10.1109\/TIP.2020.3021291"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, \u201cBeyond a Gaussian denoiser: Residual learning of deep CNN for image denoising,\u201d IEEE Trans. Image Process., vol.26, no.7, pp.3142-3155, Feb. 2017. 10.1109\/tip.2017.2662206","DOI":"10.1109\/TIP.2017.2662206"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] K. Zhang, W. Zuo, and L. Zhang, \u201cFFDNet: Toward a fast and flexible solution for CNN-based image denoising,\u201d IEEE Trans. Image Process., vol.27, no.9, pp.4608-4622, May 2018. 10.1109\/tip.2018.2839891","DOI":"10.1109\/TIP.2018.2839891"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] M. Zhao, G. Cao, X. Huang, and L. Yang, \u201cHybrid transformer-CNN for real image denoising,\u201d IEEE Signal Process. Lett., vol.29, pp.1252-1256, May 2022. 10.1109\/lsp.2022.3176486","DOI":"10.1109\/LSP.2022.3176486"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] F. Jia, L. Ma, Y. Yang, and T. Zeng, \u201cPixel-attention CNN with color correlation loss for color image denoising,\u201d IEEE Signal Process. Lett., vol.28, pp.1600-1604, July 2021. 10.1109\/lsp.2021.3100263","DOI":"10.1109\/LSP.2021.3100263"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] Y. Song, Y. Zhu, and X. Du, \u201cGrouped multi-scale network for real-world image denoising,\u201d IEEE Signal Process. Lett., vol.27, pp.2124-2128, Nov. 2020. 10.1109\/lsp.2020.3039726","DOI":"10.1109\/LSP.2020.3039726"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] T. Liu, D. Hu, Z. Wang, J. Gou, and W. Chen, \u201cHyperspectral image denoising using nonconvex fraction function,\u201d IEEE Geosci. Remote Sens. Lett., vol.20, pp.1-5, Art. no.5508105, Aug. 2023. 10.1109\/lgrs.2023.3307411","DOI":"10.1109\/LGRS.2023.3307411"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] Y. Shi, T. Liu, D. Hu, C. Li, and Z. Wang, \u201cNonconvex regularization with multi-weighted strategy for real color image denoising,\u201d Int. J. Intell. Syst., vol.2023, Art. no.8813500, Sept. 2023. 10.1155\/2023\/8813500","DOI":"10.1155\/2023\/8813500"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] S. Wang, L. Zhang, and Y. Liang, \u201cNonlocal spectral prior model for low-level vision,\u201d Asi. Conf. Comput. Vis., pp.231-244, Nov. 2012. 10.1007\/978-3-642-37431-9_18","DOI":"10.1007\/978-3-642-37431-9_18"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] D.L. Donoho, \u201cCompressed sensing,\u201d IEEE Trans. Inf. Theory, vol.52, no.4, pp.1289-1306, 2006. 10.1109\/tit.2006.871582","DOI":"10.1109\/TIT.2006.871582"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] E.J. Candes and M.B. Wakin, \u201cAn introduction to compressive sampling,\u201d IEEE Signal Process. Mag., vol.25, no.2, pp.21-30, 2008. 10.1109\/msp.2007.914731","DOI":"10.1109\/MSP.2007.914731"},{"key":"29","unstructured":"[29] M. Fazel, Matrix rank minimization with applications, Ph.D. thesis, Stanford University, 2002."},{"key":"30","doi-asserted-by":"publisher","unstructured":"[30] E.J. Cand\u00e8s, X. Li, Y. Ma, and J. Wright, \u201cRobust principal component analysis?,\u201d J. ACM, vol.58, no.3, Art. no.11, pp.1-37, 2011. 10.1145\/1970392.1970395","DOI":"10.1145\/1970392.1970395"},{"key":"31","doi-asserted-by":"publisher","unstructured":"[32] J.-F. Cai, E.J. Cand\u00e8s, and Z. Shen, \u201cA singular value thresholding algorithm for matrix completion,\u201d SIAM J. Optim., vol.20, no.4, pp.1956-1982, March 2010. 10.1137\/080738970","DOI":"10.1137\/080738970"},{"key":"32","unstructured":"[33] K. Toh and S. Yun, \u201cAn accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems,\u201d Pac. J. Optim., vol.6, no.615-640, p.15, 2010."},{"key":"33","doi-asserted-by":"publisher","unstructured":"[34] F. Nie, H. Huang, and C. Ding, \u201cLow-rank matrix recovery via efficient Schatten p-norm minimization,\u201d AAAI Conf. Artif. Intell., vol.26, no.1, pp.655-661, 2021. 10.1609\/aaai.v26i1.8210","DOI":"10.1609\/aaai.v26i1.8210"},{"key":"34","doi-asserted-by":"publisher","unstructured":"[35] Y. Hu, D. Zhang, J. Ye, X. Li, and X. He, \u201cFast and accurate matrix completion via truncated nuclear norm regularization,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.35, no.9, pp.2117-2130, 2013. 10.1109\/tpami.2012.271","DOI":"10.1109\/TPAMI.2012.271"},{"key":"35","doi-asserted-by":"publisher","unstructured":"[36] T.-H. Oh, Y.-W. Tai, J.-C. Bazin, H. Kim, and I.S. Kweon, \u201cPartial sum minimization of singular values in robust PCA: Algorithm and applications,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.38, no.4, pp.744-758, April 2016. 10.1109\/tpami.2015.2465956","DOI":"10.1109\/TPAMI.2015.2465956"},{"key":"36","doi-asserted-by":"crossref","unstructured":"[37] Q. Sun, S. Xiang, and J. Ye, \u201cRobust principal component analysis via capped norms,\u201d 19th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, New York, NY, USA, pp.311-319, Association for Computing Machinery, 2013. 10.1145\/2487575.2487604","DOI":"10.1145\/2487575.2487604"},{"key":"37","doi-asserted-by":"publisher","unstructured":"[38] F. Zhang, Z. Yang, Y. Chen, J. Yang, and G. Yang, \u201cMatrix completion via capped nuclear norm,\u201d IET Image Process., vol.12, no.6, pp.959-966, 2018. 10.1049\/iet-ipr.2017.0515","DOI":"10.1049\/iet-ipr.2017.0515"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[39] W. Zuo, D. Meng, L. Zhang, X. Feng, and D. Zhang, \u201cA generalized iterated shrinkage algorithm for non-convex sparse coding,\u201d IEEE Int. Conf. Comput. Vis., pp.217-224, 2013. 10.1109\/iccv.2013.34","DOI":"10.1109\/ICCV.2013.34"},{"key":"39","doi-asserted-by":"publisher","unstructured":"[40] Z. Zha, X. Yuan, B. Wen, J. Zhou, J. Zhang, and C. Zhu, \u201cFrom rank estimation to rank approximation: Rank residual constraint for image restoration,\u201d IEEE Trans. Image Process., vol.29, pp.3254-3269, 2020. 10.1109\/tip.2019.2958309","DOI":"10.1109\/TIP.2019.2958309"},{"key":"40","doi-asserted-by":"publisher","unstructured":"[41] T. Zhang, D. Wu, and X. Mo, \u201cThe rank residual constraint model with weighted Schatten <i>p<\/i>-norm minimization for image denoising,\u201d Circuits Syst. Signal Process., vol.42, pp.4740-4758, 2023. 10.1007\/s00034-023-02330-5","DOI":"10.1007\/s00034-023-02330-5"},{"key":"41","doi-asserted-by":"publisher","unstructured":"[42] Y. Lou and M. Yan, \u201cFast <i>L<\/i><sub>1<\/sub>-<i>L<\/i><sub>2<\/sub> minimization via a proximal operator,\u201d SIAM J. Sci. Comput., vol.74, no.2, pp.767-785, 2018. 10.1007\/s10915-017-0463-2","DOI":"10.1007\/s10915-017-0463-2"},{"key":"42","doi-asserted-by":"crossref","unstructured":"[43] J. Xu, L. Zhang, D. Zhang, and X. Feng, \u201cMulti-channel weighted nuclear norm minimization for real color image denoising,\u201d IEEE Int. Conf. Comput. Vis., pp.1105-1113, 2017. 10.1109\/iccv.2017.125","DOI":"10.1109\/ICCV.2017.125"},{"key":"43","doi-asserted-by":"crossref","unstructured":"[44] X. Huang, B. Du, and W. Liu, \u201cMultichannel color image denoising via weighted Schatten p-norm minimization,\u201d Int. Joint Conf. Artif. Intell., pp.637-644, 2020. 10.24963\/ijcai.2020\/89","DOI":"10.24963\/ijcai.2020\/89"},{"key":"44","doi-asserted-by":"crossref","unstructured":"[45] A. Buades, B. Coll, and J.-M. Morel, \u201cA non-local algorithm for image denoising,\u201d IEEE Conf. Comput. Vis. Pattern Recognit., vol.2, pp.60-65, 2005. 10.1109\/cvpr.2005.38","DOI":"10.1109\/CVPR.2005.38"},{"key":"45","doi-asserted-by":"crossref","unstructured":"[46] Y. Wang, Q. Yao, and J. Kwok, \u201cA scalable, adaptive and sound nonconvex regularizer for low-rank matrix learning,\u201d Int. World Wide Web Conf., pp.1798-1808, 2021. 10.1145\/3442381.3450142","DOI":"10.1145\/3442381.3450142"},{"key":"46","doi-asserted-by":"publisher","unstructured":"[47] L. Mirsky, \u201cA trace inequality of John von Neumann,\u201d Monatshefte f\u00fcr mathematik, vol.79, pp.303-306, 1975. 10.1007\/bf01647331","DOI":"10.1007\/BF01647331"},{"key":"47","doi-asserted-by":"publisher","unstructured":"[48] H. Talebi and P. Milanfar, \u201cGlobal image denoising,\u201d IEEE Trans. Image Process., vol.23, no.2, pp.755-768, 2014. 10.1109\/tip.2013.2293425","DOI":"10.1109\/TIP.2013.2293425"},{"key":"48","doi-asserted-by":"publisher","unstructured":"[49] J. Pang and G. Cheung, \u201cGraph Laplacian regularization for image denoising: Analysis in the continuous domain,\u201d IEEE Trans. Image Process., vol.26, no.4, pp.1770-1785, 2017. 10.1109\/tip.2017.2651400","DOI":"10.1109\/TIP.2017.2651400"},{"key":"49","doi-asserted-by":"publisher","unstructured":"[50] Z. Zha, X. Yuan, B. Wen, J. Zhang, and C. Zhu, \u201cNonconvex structural sparsity residual constraint for image restoration,\u201d IEEE Trans. Cybern., vol.52, no.11, pp.12440-12453, 2022. 10.1109\/tcyb.2021.3084931","DOI":"10.1109\/TCYB.2021.3084931"},{"key":"50","doi-asserted-by":"publisher","unstructured":"[51] D.M. Vo, T.P. Le, D.M. Nguyen, and S.-W. Lee, \u201cBoostNet: A boosted convolutional neural network for image blind denoising,\u201d IEEE Access, vol.9, pp.115145-115164, 2021. 10.1109\/access.2021.3081697","DOI":"10.1109\/ACCESS.2021.3081697"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/8\/E107.D_2023EDP7265\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T11:31:46Z","timestamp":1732534306000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/8\/E107.D_2023EDP7265\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,1]]},"references-count":50,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2023edp7265","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"type":"print","value":"0916-8532"},{"type":"electronic","value":"1745-1361"}],"subject":[],"published":{"date-parts":[[2024,8,1]]},"article-number":"2023EDP7265"}}