{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:21:01Z","timestamp":1761582061153,"version":"3.37.3"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,2,16]],"date-time":"2021-02-16T00:00:00Z","timestamp":1613433600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,2,16]],"date-time":"2021-02-16T00:00:00Z","timestamp":1613433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100012129","name":"China Aerospace Science and Technology Corporation","doi-asserted-by":"crossref","award":["Young Top Talents Foundation"],"award-info":[{"award-number":["Young Top Talents Foundation"]}],"id":[{"id":"10.13039\/501100012129","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773383"],"award-info":[{"award-number":["61773383"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s11760-021-01866-w","type":"journal-article","created":{"date-parts":[[2021,2,19]],"date-time":"2021-02-19T01:00:45Z","timestamp":1613696445000},"page":"1351-1359","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Fusion diversion network for fast, accurate and lightweight single image super-resolution"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5322-9760","authenticated-orcid":false,"given":"Zheng","family":"Gu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liping","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanhong","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tieying","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,16]]},"reference":[{"issue":"8","key":"1866_CR1","doi-asserted-by":"publisher","first-page":"2226","DOI":"10.1109\/TIP.2006.877407","volume":"15","author":"L Zhang","year":"2006","unstructured":"Zhang, L., Wu, X.: An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans. Image Process. 15(8), 2226\u20132238 (2006)","journal-title":"IEEE Trans. Image Process."},{"issue":"11","key":"1866_CR2","doi-asserted-by":"publisher","first-page":"4544","DOI":"10.1109\/TIP.2012.2208977","volume":"21","author":"K Zhang","year":"2012","unstructured":"Zhang, K., Gao, X., Tao, D., Li, X.: Single image super-resolution with non-local means and steering kernel regression. IEEE Trans. Image Process. 21(11), 4544\u20134556 (2012)","journal-title":"IEEE Trans. Image Process."},{"key":"1866_CR3","doi-asserted-by":"crossref","unstructured":"Timofte, R., De, V., Gool, L.V.: Anchored neighborhood regression for fast example-based super-resolution. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), pp. 1920\u20131927 (2013).","DOI":"10.1109\/ICCV.2013.241"},{"issue":"6","key":"1866_CR4","doi-asserted-by":"publisher","first-page":"2569","DOI":"10.1109\/TIP.2014.2305844","volume":"23","author":"T Peleg","year":"2014","unstructured":"Peleg, T., Elad, M.: A statistical prediction model based on sparse representations for single image super-resolution. IEEE Trans. Image Process. 23(6), 2569\u20132582 (2014)","journal-title":"IEEE Trans. Image Process."},{"key":"1866_CR5","doi-asserted-by":"crossref","unstructured":"Timofte, R., De Smet, V., Van Gool L.: A+: adjusted anchored neighborhood regression for fast super-resolution. In: Proceedings of Asian Conference on Computer Vision (ACCV), pp.111\u2013126 (2014).","DOI":"10.1007\/978-3-319-16817-3_8"},{"key":"1866_CR6","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 (CVPR), pp. 5197\u20135206 (2015).","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"1866_CR7","doi-asserted-by":"crossref","unstructured":"Schulter, S., Leistner, C., Bischof, H.: Fast and accurate image upscaling with super-resolution forests. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3791\u20133799 (2015).","DOI":"10.1109\/CVPR.2015.7299003"},{"issue":"2","key":"1866_CR8","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":"1866_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":"1866_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":"1866_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 (CVPR), pp. 2790\u20132798 (2017).","DOI":"10.1109\/CVPR.2017.298"},{"issue":"11","key":"1866_CR12","doi-asserted-by":"publisher","first-page":"2599","DOI":"10.1109\/TPAMI.2018.2865304","volume":"41","author":"W-S Lai","year":"2019","unstructured":"Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: Fast and accurate image super-resolution with deep Laplacian pyramid networks. IEEE Trans. Pattern Anal. Mach. Intell. 41(11), 2599\u20132613 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1866_CR13","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 Workshops (CVPRW), pp. 1132\u20131140 (2017).","DOI":"10.1109\/CVPRW.2017.151"},{"key":"1866_CR14","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., Liu, X., Xu, C.: MemNet: a persistent memory network for image restoration. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 4549\u20134557 (2017).","DOI":"10.1109\/ICCV.2017.486"},{"key":"1866_CR15","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 (CVPR), pp. 2472\u20132481 (2018).","DOI":"10.1109\/CVPR.2018.00262"},{"key":"1866_CR16","doi-asserted-by":"crossref","unstructured":"Haris, M., Shakhnarovich, G., Ukita, N.: Deep back-projection networks for super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1664\u20131673 (2018).","DOI":"10.1109\/CVPR.2018.00179"},{"key":"1866_CR17","doi-asserted-by":"publisher","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). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_18 (2018).","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"1866_CR18","doi-asserted-by":"crossref","unstructured":"Li, Z., Yang, J., Liu, Z., Yang, X., Jeon, G., 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"},{"key":"1866_CR19","doi-asserted-by":"publisher","unstructured":"Ahn, N., Kang, B., Sohn. K.-A.: Fast, accurate, and lightweight super-resolution with cascading residual network. In: Proceedings of the European Conference on Computer Vision (ECCV). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_16 (2018).","DOI":"10.1007\/978-3-030-01249-6_16"},{"key":"1866_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.02.067","author":"D Lin","year":"2019","unstructured":"Lin, D., et al.: SCRSR: an efficient recursive convolutional neural network for fast and accurate image super-resolution. Neurocomputing (2019). https:\/\/doi.org\/10.1016\/j.neucom.2019.02.067","journal-title":"Neurocomputing"},{"key":"1866_CR21","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 (CVPR), pp. 723\u2013731 (2018).","DOI":"10.1109\/CVPR.2018.00082"},{"key":"1866_CR22","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/s11760-019-01569-3","volume":"14","author":"H Nasrollahi","year":"2020","unstructured":"Nasrollahi, H., Farajzadeh, K., Hosseini, V., et al.: Deep artifact-free residual network for single-image super-resolution. Signal Image Video Process. 14, 407\u2013415 (2020). https:\/\/doi.org\/10.1007\/s11760-019-01569-3","journal-title":"Signal Image Video Process."},{"key":"1866_CR23","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2020.2980172","author":"H Kim","year":"2020","unstructured":"Kim, H., Kim, G.: Single image super-resolution using fire modules with asymmetric configuration. IEEE Signal Process. Lett. (2020). https:\/\/doi.org\/10.1109\/LSP.2020.2980172","journal-title":"IEEE Signal Process. Lett."},{"issue":"4","key":"1866_CR24","first-page":"212","volume":"3","author":"GE Hinton","year":"2012","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. Comput. Sci. 3(4), 212\u2013223 (2012)","journal-title":"Comput. Sci."},{"key":"1866_CR25","first-page":"249","volume":"9","author":"X Glorot","year":"2010","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. J. Mach. Learn. Res. 9, 249\u2013256 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"1866_CR26","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R.: NTIRE 2017 challenge on single image super-resolution: dataset and study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1122\u20131131 (2017).","DOI":"10.1109\/CVPRW.2017.150"},{"key":"1866_CR27","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., Morel, M.-l. A.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: Proceedings of the British Machine Vision Conference, pp. 1\u201310 (2012).","DOI":"10.5244\/C.26.135"},{"key":"1866_CR28","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, ser. Curves and Surfaces, pp. 711\u2013730 (2012).","DOI":"10.1007\/978-3-642-27413-8_47"},{"key":"1866_CR29","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 International Conference on Computer Vision (ICCV), vol. 2, pp. 416\u2013423 (2001)","DOI":"10.1109\/ICCV.2001.937655"},{"key":"1866_CR30","doi-asserted-by":"crossref","unstructured":"Huang, J., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5197\u20135206 (2015).","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"1866_CR31","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization, arXiv e-prints, arXiv:1412.6980. In: Proceedings of the International Conference for Learning Representations (2014)."},{"key":"1866_CR32","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Grishick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675\u2013678 (2014).","DOI":"10.1145\/2647868.2654889"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-021-01866-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-021-01866-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-021-01866-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T12:40:21Z","timestamp":1724503221000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-021-01866-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,16]]},"references-count":32,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["1866"],"URL":"https:\/\/doi.org\/10.1007\/s11760-021-01866-w","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2021,2,16]]},"assertion":[{"value":"17 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}