{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T05:18:30Z","timestamp":1768108710385,"version":"3.49.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T00:00:00Z","timestamp":1645056000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T00:00:00Z","timestamp":1645056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100003398","name":"Shanxi Scholarship Council of China","doi-asserted-by":"publisher","award":["2020-111"],"award-info":[{"award-number":["2020-111"]}],"id":[{"id":"10.13039\/501100003398","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s11760-022-02136-z","type":"journal-article","created":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T09:02:56Z","timestamp":1645088576000},"page":"1793-1801","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A lightweight multi-scale residual network for single image super-resolution"],"prefix":"10.1007","volume":"16","author":[{"given":"Xiaole","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4636-8084","authenticated-orcid":false,"given":"Ruifeng","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Chenxia","family":"Guo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,17]]},"reference":[{"issue":"6","key":"2136_CR1","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/TASSP.1981.1163711","volume":"29","author":"R Keys","year":"1981","unstructured":"Keys, R.: Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust. Speech Signal Process. 29(6), 1153\u20131160 (1981)","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"2136_CR2","doi-asserted-by":"crossref","unstructured":"Zhou, R., and Susstrunk S.: Kernel modeling super-resolution on real low-resolution images. In: Proceedings of IEEE Internati4onal Conference on Computer Vision (ICCV), pp. 2433\u20132443 (2019)","DOI":"10.1109\/ICCV.2019.00252"},{"key":"2136_CR3","doi-asserted-by":"crossref","unstructured":"Gu, J., Lu, H., Zuo, W.M., Dong, C.: Blind super-resolution with iterative kernel correction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1604\u20131613 (2019)","DOI":"10.1109\/CVPR.2019.00170"},{"key":"2136_CR4","doi-asserted-by":"crossref","unstructured":"Li, B., Liu, R., Cao, J., Zhang, J., Lai, Y.-K.: Liu, X. Online low-rank representation learning for joint multi-subspace recovery and clustering. IEEE Trans. Image Process. 27(1), 335\u2013348 (2018)","DOI":"10.1109\/TIP.2017.2760510"},{"key":"2136_CR5","doi-asserted-by":"crossref","unstructured":"Huang, J.-B, Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pp. 5197\u20135206 (2015)","DOI":"10.1109\/CVPR.2015.7299156"},{"issue":"2","key":"2136_CR6","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2016","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295\u2013307 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2136_CR7","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: European conference on computer vision. Springer, pp 391\u2013407 (2016)","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"2136_CR8","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Huszar, F., Totz, J., Andrew, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1874\u20131883 (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"2136_CR9","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1646\u20131654 (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"2136_CR10","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Deeply-recursive convolutional network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1637\u20131645 (2016)","DOI":"10.1109\/CVPR.2016.181"},{"key":"2136_CR11","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., Liu, X.: Image super-resolution via deep recursive residual network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3147\u20133155 (2017)","DOI":"10.1109\/CVPR.2017.298"},{"key":"2136_CR12","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1132\u20131140 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"2136_CR13","doi-asserted-by":"crossref","unstructured":"Tong, T., Li, G., Liu, X., Gao, Q.: Image super-resolution using dense skip connections. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 4 809\u20134 817 (2017)","DOI":"10.1109\/ICCV.2017.514"},{"key":"2136_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2472\u20132481 (2018)","DOI":"10.1109\/CVPR.2018.00262"},{"key":"2136_CR15","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., Liu, X., Xu, C.: Memnet: A Persistent Memory Network for Image Restoration. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), pp. 4549\u20134557 (2017)","DOI":"10.1109\/ICCV.2017.486"},{"key":"2136_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 286\u2013301 (2018)","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"2136_CR17","doi-asserted-by":"crossref","unstructured":"Ahn, N., Kang, B., and Sohn, K.-A.: Fast, accurate, and lightweight super-resolution with cascading residual network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 252\u2013268 (2018)","DOI":"10.1007\/978-3-030-01249-6_16"},{"key":"2136_CR18","doi-asserted-by":"crossref","unstructured":"Hui, Z., Wang, X., Gao, X.: Fast and accurate single image super-resolution via information distillation network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 723\u2013731 (2018)","DOI":"10.1109\/CVPR.2018.00082"},{"key":"2136_CR19","doi-asserted-by":"publisher","first-page":"8368","DOI":"10.1109\/TIP.2020.3014953","volume":"29","author":"B Li","year":"2020","unstructured":"Li, B., Wang, B., Liu, J., Qi, Z., Shi, Y.: s-LWSR: Super lightweight super-resolution network. IEEE Trans. Image Process. 29, 8368\u20138380 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"2136_CR20","doi-asserted-by":"crossref","unstructured":"Wang, Z., Gao, G., Li, J., Yu, Y., Lu, H.: Lightweight image super-resolution with multi-scale feature interaction network. In: 2021 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136 (2021)","DOI":"10.1109\/ICME51207.2021.9428136"},{"issue":"3","key":"2136_CR21","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1109\/TCYB.2020.2970104","volume":"51","author":"R Lan","year":"2020","unstructured":"Lan, R., Sun, L., Liu, Z., Lu, H., Pang, C., Luo, X.: MADNet: A fast and lightweight network for single-image super resolution. In IEEE T. Cybernetics 51(3), 1443\u20131453 (2020)","journal-title":"In IEEE T. Cybernetics"},{"key":"2136_CR22","doi-asserted-by":"crossref","unstructured":"Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.624\u2013632(2017)","DOI":"10.1109\/CVPR.2017.618"},{"key":"2136_CR23","doi-asserted-by":"crossref","unstructured":"Li, J., Fang, F., Mei, K., Zhang, G.: Multi-scale residual network for image super-resolution. In: European Conference on Computer Vision (ECCV), pp.527\u2013542 (2018)","DOI":"10.1007\/978-3-030-01237-3_32"},{"key":"2136_CR24","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1007\/s10489-020-01869-z","volume":"51","author":"C Xiong","year":"2021","unstructured":"Xiong, C., Shi, X., Gao, Z., Wang, G.: Attention augmented multi-scale network for single image super-resolution. Appl. Intell. 51, 935\u2013951 (2021)","journal-title":"Appl. Intell."},{"key":"2136_CR25","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Der Maaten, L. V., Weinberger, K. Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261\u20132269 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"2136_CR26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1026\u20131034 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"2136_CR27","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 7132\u20137141(2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2136_CR28","doi-asserted-by":"crossref","unstructured":"Dai, T., Cai, J., Zhang, Y, Xia, S., Zhang, L.: Second-order attention network for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11065\u201311074 (2019)","DOI":"10.1109\/CVPR.2019.01132"},{"key":"2136_CR29","doi-asserted-by":"crossref","unstructured":"Niu, B., Wen, W., Ren, W., Zhang, X., Yang, L., Wang, S., Shen, H.: Single image super-resolution via a holistic attention network. In: European Conference on Computer Vision (ECCV), pp. 191\u2013207 (2020)","DOI":"10.1007\/978-3-030-58610-2_12"},{"key":"2136_CR30","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R.: Ntire 2017 challenge on single image super-resolution: Dataset and study. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 111\u20131121 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"2136_CR31","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., Alberimorel, M.L.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In Proceedings of the British Machine Vision Conference (BMVC), pp. 1\u201310 (2012)","DOI":"10.5244\/C.26.135"},{"key":"2136_CR32","doi-asserted-by":"crossref","unstructured":"Zeyde, R., Elad, M., Protter. M.: On single image scale-up using sparse-representations. In: Proceedings of the International Conference on Curves and Surfaces, pp.711\u2013730 (2010)","DOI":"10.1007\/978-3-642-27413-8_47"},{"key":"2136_CR33","doi-asserted-by":"crossref","unstructured":"Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 416\u2013423 (2001)","DOI":"10.1109\/ICCV.2001.937655"},{"issue":"1","key":"2136_CR34","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","volume":"3","author":"H Zhao","year":"2017","unstructured":"Zhao, H., Gallo, O., Frosio, I., Kautz, J.: Loss functions for image restoration with neural networks. IEEE Trans. Comput. Imaging. 3(1), 47\u201357 (2017)","journal-title":"IEEE Trans. Comput. Imaging."},{"key":"2136_CR35","doi-asserted-by":"crossref","unstructured":"Li, Z., Yang, J., Liu, Z., Yang, X. Jeon, G. and Wu, W.: Feedback network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3867\u20133876 (2019)","DOI":"10.1109\/CVPR.2019.00399"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02136-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-022-02136-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02136-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T20:22:50Z","timestamp":1726690970000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-022-02136-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,17]]},"references-count":35,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["2136"],"URL":"https:\/\/doi.org\/10.1007\/s11760-022-02136-z","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,17]]},"assertion":[{"value":"16 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 December 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 January 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"All authors have agreed to participate.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}