{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T21:12:49Z","timestamp":1757452369471,"version":"3.41.0"},"reference-count":35,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T00:00:00Z","timestamp":1623196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2018AAA0100102 and 2018AAA0100100"],"award-info":[{"award-number":["2018AAA0100102 and 2018AAA0100100"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61972212, 61772568, 61833011, and 61806098"],"award-info":[{"award-number":["61972212, 61772568, 61833011, and 61806098"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100010014","name":"Six Talent Peaks Project in Jiangsu Province","doi-asserted-by":"crossref","award":["RJFW-011"],"award-info":[{"award-number":["RJFW-011"]}],"id":[{"id":"10.13039\/501100010014","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"crossref","award":["BK20190089"],"award-info":[{"award-number":["BK20190089"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Computer Information Processing Technology","award":["KJS1840"],"award-info":[{"award-number":["KJS1840"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Technol."],"published-print":{"date-parts":[[2021,8,31]]},"abstract":"<jats:p>\n            Super-resolution methods for facial image via representation learning scheme have become very effective methods due to their efficiency. The key problem for the super-resolution of facial image is to reveal the latent relationship between the\n            <jats:bold>low-resolution<\/jats:bold>\n            (\n            <jats:bold>LR<\/jats:bold>\n            ) and the corresponding\n            <jats:bold>high-resolution<\/jats:bold>\n            (\n            <jats:bold>HR<\/jats:bold>\n            ) training patch pairs. To simultaneously utilize the contextual information of the target position and the manifold structure of the primitive HR space, in this work, we design a robust context-patch facial image super-resolution scheme via a\n            <jats:bold>kernel locality-constrained coupled-layer regression<\/jats:bold>\n            (KLC2LR) scheme to obtain the desired HR version from the acquired LR image. Here, KLC2LR proposes to acquire contextual surrounding patches to represent the target patch and adds an HR layer constraint to compensate the detail information. Additionally, KLC2LR desires to acquire more high-frequency information by searching for nearest neighbors in the HR sample space. We also utilize kernel function to map features in original low-dimensional space into a high-dimensional one to obtain potential nonlinear characteristics. Our compared experiments in the noisy and noiseless cases have verified that our suggested methodology performs better than many existing predominant facial image super-resolution methods.\n          <\/jats:p>","DOI":"10.1145\/3418462","type":"journal-article","created":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T14:41:16Z","timestamp":1623249676000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Robust Facial Image Super-Resolution by Kernel Locality-Constrained Coupled-Layer Regression"],"prefix":"10.1145","volume":"21","author":[{"given":"Guangwei","family":"Gao","sequence":"first","affiliation":[{"name":"Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, China and Digital Content and Media Sciences Research Division, National Institute of Informatics, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huimin","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Yu","sequence":"additional","affiliation":[{"name":"Digital Content and Media Sciences Research Division, National Institute of Informatics, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heyou","family":"Chang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Trusted Cloud Computing and Big Data Analysis, Nanjing XiaoZhuang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Yue","sequence":"additional","affiliation":[{"name":"College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,6,9]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/795661.796208"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2002.1033210"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3379930"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2007.893346"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00232"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2439281"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2012.12.059"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2016.12.021"},{"key":"e_1_2_1_9_1","volume-title":"Constructing multilayer locality-constrained matrix regression framework for noise robust face super-resolution.Pattern Recogn. (July","author":"Gao Guangwei","year":"2020","unstructured":"Guangwei Gao , Yi Yu , Jin Xie , Jian Yang , Meng Yang , and Jian Zhang . 2020. Constructing multilayer locality-constrained matrix regression framework for noise robust face super-resolution.Pattern Recogn. (July 2020 ), 107539. Guangwei Gao, Yi Yu, Jin Xie, Jian Yang, Meng Yang, and Jian Zhang. 2020. Constructing multilayer locality-constrained matrix regression framework for noise robust face super-resolution.Pattern Recogn. (July 2020), 107539."},{"key":"e_1_2_1_10_1","volume-title":"Cross-resolution face recognition with pose variations via multilayer locality-constrained structural orthogonal procrustes regression.Inf. Sci. 506 (January","author":"Gao Guangwei","year":"2020","unstructured":"Guangwei Gao , Yi Yu , Meng Yang , Heyou Chang , Pu Huang , and Dong Yue . 2020. Cross-resolution face recognition with pose variations via multilayer locality-constrained structural orthogonal procrustes regression.Inf. Sci. 506 (January 2020 ), 19\u201336. Guangwei Gao, Yi Yu, Meng Yang, Heyou Chang, Pu Huang, and Dong Yue. 2020. Cross-resolution face recognition with pose variations via multilayer locality-constrained structural orthogonal procrustes regression.Inf. Sci. 506 (January 2020), 19\u201336."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCA.2007.909557"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3351082"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3381750"},{"key":"e_1_2_1_14_1","first-page":"1","article-title":"Face hallucination using cascaded super-resolution and identity priors.IEEE Trans","volume":"29","author":"Grm Klemen","year":"2019","unstructured":"Klemen Grm , Walter J. Scheirer , and Vitomir \u0160truc . 2019 . Face hallucination using cascaded super-resolution and identity priors.IEEE Trans . Image Proc. 29 , 1 (October 2019), 2150\u20132165. Klemen Grm, Walter J. Scheirer, and Vitomir \u0160truc. 2019. Face hallucination using cascaded super-resolution and identity priors.IEEE Trans. Image Proc. 29, 1 (October 2019), 2150\u20132165.","journal-title":"Image Proc."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2005.860599"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2010.579"},{"key":"e_1_2_1_17_1","volume-title":"Face hallucination from low quality images using definition-scalable inference.Pattern Recogn. 94 (October","author":"Hu Xiao","year":"2019","unstructured":"Xiao Hu , Peirong Ma , Zhuohao Mai , Shaohu Peng , Zhao Yang , and Li Wang . 2019. Face hallucination from low quality images using definition-scalable inference.Pattern Recogn. 94 (October 2019 ), 110\u2013121. Xiao Hu, Peirong Ma, Zhuohao Mai, Shaohu Peng, Zhao Yang, and Li Wang. 2019. Face hallucination from low quality images using definition-scalable inference.Pattern Recogn. 94 (October 2019), 110\u2013121."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2008.922421"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2014.2347201"},{"key":"e_1_2_1_20_1","first-page":"5","article-title":"Noise robust face hallucination via locality-constrained representation.IEEE Trans","volume":"16","author":"Jiang Junjun","year":"2014","unstructured":"Junjun Jiang , Ruimin Hu , Zhongyuan Wang , and Zhen Han . 2014 . Noise robust face hallucination via locality-constrained representation.IEEE Trans . Multimedia 16 , 5 (August 2014), 1268\u20131281. Junjun Jiang, Ruimin Hu, Zhongyuan Wang, and Zhen Han. 2014. Noise robust face hallucination via locality-constrained representation.IEEE Trans. Multimedia 16, 5 (August 2014), 1268\u20131281.","journal-title":"Multimedia"},{"key":"e_1_2_1_21_1","first-page":"11","article-title":"Noise robust face image super-resolution through smooth sparse representation.IEEE","volume":"47","author":"Jiang Junjun","year":"2017","unstructured":"Junjun Jiang , Jiayi Ma , Chen Chen , Xinwei Jiang , and Zheng Wang . 2017 . Noise robust face image super-resolution through smooth sparse representation.IEEE Trans. Cybern. 47 , 11 (November 2017), 3991\u20134002. Junjun Jiang, Jiayi Ma, Chen Chen, Xinwei Jiang, and Zheng Wang. 2017. Noise robust face image super-resolution through smooth sparse representation.IEEE Trans. Cybern. 47, 11 (November 2017), 3991\u20134002.","journal-title":"Trans. Cybern."},{"key":"e_1_2_1_22_1","first-page":"1","article-title":"Context-patch based face hallucination via thresholding locality-constrained representation and reproducing learning.IEEE","volume":"50","author":"Jiang Junjun","year":"2019","unstructured":"Junjun Jiang , Yi Yu , Suhua Tang , Jiayi Ma , Akiko Aizawa , and Kiyoharu Aizawa . 2019 . Context-patch based face hallucination via thresholding locality-constrained representation and reproducing learning.IEEE Trans. Cybern. 50 , 1 (January 2019), 324\u2013337. Junjun Jiang, Yi Yu, Suhua Tang, Jiayi Ma, Akiko Aizawa, and Kiyoharu Aizawa. 2019. Context-patch based face hallucination via thresholding locality-constrained representation and reproducing learning.IEEE Trans. Cybern. 50, 1 (January 2019), 324\u2013337.","journal-title":"Trans. Cybern."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3338533.3366576"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2018.8451122"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_17"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2004.1261081"},{"key":"e_1_2_1_27_1","volume-title":"Global-local fusion network for face super-resolution.Neurocomputing 387 (April","author":"Lu Tao","year":"2020","unstructured":"Tao Lu , Jiaming Wang , Junjun Jiang , and Yanduo Zhang . 2020. Global-local fusion network for face super-resolution.Neurocomputing 387 (April 2020 ), 309\u2013320. Tao Lu, Jiaming Wang, Junjun Jiang, and Yanduo Zhang. 2020. 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