{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T10:25:23Z","timestamp":1760955923427,"version":"3.41.0"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2019,2,25]],"date-time":"2019-02-25T00:00:00Z","timestamp":1551052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Tianjin Research Program of Application Foundation and Advanced Technology","award":["15JCYBJC16500"],"award-info":[{"award-number":["15JCYBJC16500"]}]},{"name":"Program for Innovative Research Team in University of Tianjin","award":["TD13-5034"],"award-info":[{"award-number":["TD13-5034"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2019,2,28]]},"abstract":"<jats:p>Single-image super-resolution (SISR) methods based on convolutional neural networks (CNN) have shown great potential in the literature. However, most deep CNN models don\u2019t have direct access to subsequent layers, seriously hindering the information flow. Furthermore, they fail to make full use of the hierarchical features from different low-level layers, thereby resulting in relatively low accuracy. In this article, we present a new SISR CNN, called SymSR, which incorporates symmetrical nested residual connections to improve both the accuracy and the execution speed. SymSR takes a larger image region for contextual spreading. It symmetrically combines multiple short paths for the forward propagation to improve the accuracy and for the backward propagation of gradient flow to accelerate the convergence speed. Extensive experiments based on open challenge datasets show the effectiveness of symmetrical residual connections. Compared with four other state-of-the-art super-resolution CNN methods, SymSR is superior in both accuracy and runtime.<\/jats:p>","DOI":"10.1145\/3282445","type":"journal-article","created":{"date-parts":[[2019,2,25]],"date-time":"2019-02-25T13:23:28Z","timestamp":1551101008000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Symmetrical Residual Connections for Single Image Super-Resolution"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3761-8683","authenticated-orcid":false,"given":"Xianguo","family":"Li","sequence":"first","affiliation":[{"name":"Tianjin Polytechnic University; Tianjin Key Laboratory of Optoelectronic Detection Technology and System, Tianjin, China"}]},{"given":"Yemei","family":"Sun","sequence":"additional","affiliation":[{"name":"Tianjin Polytechnic University; Tianjin Key Laboratory of Optoelectronic Detection Technology and System, Tianjin, China"}]},{"given":"Yanli","family":"Yang","sequence":"additional","affiliation":[{"name":"Tianjin Polytechnic University; Tianjin Key Laboratory of Optoelectronic Detection Technology and System, Tianjin, China"}]},{"given":"Changyun","family":"Miao","sequence":"additional","affiliation":[{"name":"Tianjin Polytechnic University; Tianjin Key Laboratory of Optoelectronic Detection Technology and System, Tianjin, China"}]}],"member":"320","published-online":{"date-parts":[[2019,2,25]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.150"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5244\/C.26.135"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDSP.2013.6622796"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2439281"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1944846.1944852"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459271"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.95"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.182"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.181"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.618"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Christian Ledig Lucas Theis Ferenc Husz\u00e1r Jose Caballero Andrew Cunningham Alejandro Acosta Andrew Aitken Alykhan Tejani Johannes Totz Zehan Wang etal 2016. Photo-realistic single image super-resolution using a generative adversarial network. arXiv preprint (2016).  Christian Ledig Lucas Theis Ferenc Husz\u00e1r Jose Caballero Andrew Cunningham Alejandro Acosta Andrew Aitken Alykhan Tejani Johannes Totz Zehan Wang et al. 2016. Photo-realistic single image super-resolution using a generative adversarial network. arXiv preprint (2016).","DOI":"10.1109\/CVPR.2017.19"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299003"},{"key":"e_1_2_1_20_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman . 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 ( 2014 ). Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_2_1_21_1","volume-title":"Asian Conference on Computer Vision. Springer, 552--568","author":"Singh Abhishek","year":"2014","unstructured":"Abhishek Singh and Narendra Ahuja . 2014 . Super-resolution using sub-band self-similarity . In Asian Conference on Computer Vision. Springer, 552--568 . Abhishek Singh and Narendra Ahuja. 2014. Super-resolution using sub-band self-similarity. In Asian Conference on Computer Vision. Springer, 552--568."},{"key":"e_1_2_1_22_1","first-page":"12","article-title":"Inception-v4, inception-resnet and the impact of residual connections on learning","volume":"4","author":"Szegedy Christian","year":"2017","unstructured":"Christian Szegedy , Sergey Ioffe , Vincent Vanhoucke , and Alexander A. Alemi . 2017 . Inception-v4, inception-resnet and the impact of residual connections on learning . In AAAI , Vol. 4. 12 . Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A. Alemi. 2017. Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI, Vol. 4. 12.","journal-title":"AAAI"},{"key":"e_1_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Ying Tai Jian Yang Xiaoming Liu and Chunyan Xu. 2017. MemNet: A persistent memory network for image restoration. (2017) 4549--4557.  Ying Tai Jian Yang Xiaoming Liu and Chunyan Xu. 2017. MemNet: A persistent memory network for image restoration. (2017) 4549--4557.","DOI":"10.1109\/ICCV.2017.486"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.241"},{"key":"e_1_2_1_25_1","volume-title":"Asian Conference on Computer Vision. Springer, 111--126","author":"Timofte Radu","year":"2014","unstructured":"Radu Timofte , Vincent De Smet , and Luc Van Gool . 2014 . A+: Adjusted anchored neighborhood regression for fast super-resolution . In Asian Conference on Computer Vision. Springer, 111--126 . Radu Timofte, Vincent De Smet, and Luc Van Gool. 2014. A+: Adjusted anchored neighborhood regression for fast super-resolution. In Asian Conference on Computer Vision. Springer, 111--126."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.510"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.75"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2010.2050625"},{"key":"e_1_2_1_29_1","unstructured":"Lequan Yu Xin Yang Hao Chen Jing Qin and Pheng-Ann Heng. 2017. Volumetric ConvNets with mixed residual connections for automated prostate segmentation from 3D MR images. In AAAI. 66--72.   Lequan Yu Xin Yang Hao Chen Jing Qin and Pheng-Ann Heng. 2017. Volumetric ConvNets with mixed residual connections for automated prostate segmentation from 3D MR images. In AAAI. 66--72."},{"key":"e_1_2_1_30_1","volume-title":"Wide residual networks. arXiv preprint arXiv:1605.07146","author":"Zagoruyko Sergey","year":"2016","unstructured":"Sergey Zagoruyko and Nikos Komodakis . 2016. Wide residual networks. arXiv preprint arXiv:1605.07146 ( 2016 ). Sergey Zagoruyko and Nikos Komodakis. 2016. Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-27413-8_47"},{"key":"e_1_2_1_32_1","first-page":"1","article-title":"Adaptive residual networks for high-quality image restoration","volume":"99","author":"Zhang Y.","year":"2018","unstructured":"Y. Zhang , L. Sun , C. Yan , X. Ji , and Q. Dai . 2018 . Adaptive residual networks for high-quality image restoration . IEEE Transactions on Image Processing PP , 99 (2018), 1 -- 1 . Y. Zhang, L. Sun, C. Yan, X. Ji, and Q. Dai. 2018. Adaptive residual networks for high-quality image restoration. IEEE Transactions on Image Processing PP, 99 (2018), 1--1.","journal-title":"IEEE Transactions on Image Processing PP"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3282445","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3282445","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:57:29Z","timestamp":1750208249000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3282445"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,25]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,2,28]]}},"alternative-id":["10.1145\/3282445"],"URL":"https:\/\/doi.org\/10.1145\/3282445","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"type":"print","value":"1551-6857"},{"type":"electronic","value":"1551-6865"}],"subject":[],"published":{"date-parts":[[2019,2,25]]},"assertion":[{"value":"2018-05-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-09-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-02-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}