{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T12:08:38Z","timestamp":1770293318874,"version":"3.49.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T00:00:00Z","timestamp":1619568000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T00:00:00Z","timestamp":1619568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Scientific Research Project of Tianjin Municipal Education Commission","award":["2019KJ105"],"award-info":[{"award-number":["2019KJ105"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s10489-021-02246-0","type":"journal-article","created":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T02:12:35Z","timestamp":1619575955000},"page":"295-304","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Deformable and residual convolutional network for image super-resolution"],"prefix":"10.1007","volume":"52","author":[{"given":"Yan","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Yemei","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Shudong","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,28]]},"reference":[{"issue":"2","key":"2246_CR1","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1109\/TPAMI.2012.95","volume":"35","author":"K Jia","year":"2013","unstructured":"Jia K, Wang X, Tang X (2013) Image transformation based on learning dictionaries across image spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(2):367\u2013380","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"11","key":"2246_CR2","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","volume":"19","author":"J Yang","year":"2010","unstructured":"Yang J, Wright J, Huang TS et al (2010) Image super-resolution via sparse representation. IEEE transactions on image processing 19(11):2861\u20132873","journal-title":"IEEE transactions on image processing"},{"key":"2246_CR3","doi-asserted-by":"crossref","unstructured":"Zeyde R, Elad M, Protter M (2010) On single image scale-up using sparse-representations. In: International conference on curves and surfaces. Springer, Berlin, pp 711\u2013730","DOI":"10.1007\/978-3-642-27413-8_47"},{"key":"2246_CR4","doi-asserted-by":"crossref","unstructured":"Timofte R, De Smet V, Van Gool L (2014) A+: adjusted anchored neighborhood regression for fast super-resolution. In: Asian conference on computer vision. Springer, Cham, pp 111\u2013126","DOI":"10.1007\/978-3-319-16817-3_8"},{"key":"2246_CR5","doi-asserted-by":"crossref","unstructured":"Yang CY, Yang MH (2013) Fast direct super-resolution by simple functions. In: Proceedings of the IEEE international conference on computer vision, pp 561\u2013568","DOI":"10.1109\/ICCV.2013.75"},{"key":"2246_CR6","doi-asserted-by":"crossref","unstructured":"Schulter S, Leistner C, Bischof H (2015) Fast and accurate image upscaling with super-resolution forests. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3791\u20133799","DOI":"10.1109\/CVPR.2015.7299003"},{"issue":"2","key":"2246_CR7","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2016","unstructured":"Dong C, Loy CC, He K et al (2016) Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 38(2):295\u2013307","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2246_CR8","doi-asserted-by":"crossref","unstructured":"Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1646\u20131654","DOI":"10.1109\/CVPR.2016.182"},{"key":"2246_CR9","doi-asserted-by":"crossref","unstructured":"Kim J, Lee JK, Lee KM (2016) Deeply recursive convolutional network for image super-resolution. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp 1637\u20131645","DOI":"10.1109\/CVPR.2016.181"},{"key":"2246_CR10","doi-asserted-by":"crossref","unstructured":"Lai WS, Huang JB, Ahuja N et al (2017) Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 624\u2013632","DOI":"10.1109\/CVPR.2017.618"},{"key":"2246_CR11","doi-asserted-by":"crossref","unstructured":"Tai Y, Yang J, Liu X et al (2017) Memnet: a persistent memory network for image restoration. In: Proceedings of the IEEE international conference on computer vision, pp 4539\u20134547","DOI":"10.1109\/ICCV.2017.486"},{"key":"2246_CR12","doi-asserted-by":"crossref","unstructured":"Zhang Y, Tian Y, Kong Y et al (2018) Residual dense network for image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2472\u20132481","DOI":"10.1109\/CVPR.2018.00262"},{"key":"2246_CR13","doi-asserted-by":"crossref","unstructured":"Haris M, Shakhnarovich G, Ukita N (2018) Deep back-projection networks for super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1664\u20131673","DOI":"10.1109\/CVPR.2018.00179"},{"key":"2246_CR14","doi-asserted-by":"crossref","unstructured":"Zhang Y, Li K, Li K et al (2018) Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European conference on computer vision (ECCV), pp 286\u2013301","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"2246_CR15","doi-asserted-by":"crossref","unstructured":"Gu J, Lu H, Zuo W et al (2019) Blind super-resolution with iterative kernel correction. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1604\u20131613","DOI":"10.1109\/CVPR.2019.00170"},{"key":"2246_CR16","doi-asserted-by":"crossref","unstructured":"Soh JW, Park GY, Jo J et al (2019) Natural and realistic single image super-resolution with explicit natural manifold discrimination. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8122\u20138131","DOI":"10.1109\/CVPR.2019.00831"},{"key":"2246_CR17","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H et al (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 136\u2013144","DOI":"10.1109\/CVPRW.2017.151"},{"key":"2246_CR18","doi-asserted-by":"publisher","first-page":"106103","DOI":"10.1016\/j.knosys.2020.106103","volume":"203","author":"H Liu","year":"2020","unstructured":"Liu H, Cao F, Wen C et al (2020) Lightweight multi-scale residual networks with attention for image super-resolution. Knowledge-Based Systems 203:106103","journal-title":"Knowledge-Based Systems"},{"key":"2246_CR19","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.neunet.2020.08.008","volume":"132","author":"H Liu","year":"2020","unstructured":"Liu H, Cao F (2020) Improved dual-scale residual network for image super-resolution. Neural Netw 132:84\u201395","journal-title":"Neural Netw"},{"key":"2246_CR20","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/j.neunet.2020.09.017","volume":"132","author":"F Cao","year":"2020","unstructured":"Cao F, Yao K, Liang J (2020) Deconvolutional neural network for image super-resolution. Neural Netw 132:394\u2013404","journal-title":"Neural Netw"},{"key":"2246_CR21","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.neucom.2019.05.066","volume":"358","author":"F Cao","year":"2019","unstructured":"Cao F, Liu H (2019) Single image super-resolution via multi-scale residual channel attention network. Neurocomputing 358:424\u2013436","journal-title":"Neurocomputing"},{"key":"2246_CR22","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.knosys.2019.04.021","volume":"178","author":"F Cao","year":"2019","unstructured":"Cao F, Chen B (2019) New architecture of deep recursive convolution networks for superresolution. Knowledge-Based Systems 178:98\u2013110","journal-title":"Knowledge-Based Systems"},{"key":"2246_CR23","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, Huszr F et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4681\u20134690","DOI":"10.1109\/CVPR.2017.19"},{"key":"2246_CR24","doi-asserted-by":"crossref","unstructured":"Wang X, Yu K, Wu S et al (2018) Esrgan: enhanced super-resolution generative adversarial networks. In: Proceedings of the European conference on computer vision (ECCV), pp 63\u201379","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"2246_CR25","doi-asserted-by":"crossref","unstructured":"Shocher A, Cohen N, Irani M (2018) Zero-shot super-resolution using deep internal learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3118\u20133126","DOI":"10.1109\/CVPR.2018.00329"},{"key":"2246_CR26","doi-asserted-by":"crossref","unstructured":"Bulat A, Yang J, Tzimiropoulos G (2018) To learn image super-resolution, use a gan to learn how to do image degradation first. In: Proceedings of the European conference on computer vision (ECCV), pp 185\u2013200","DOI":"10.1007\/978-3-030-01231-1_12"},{"key":"2246_CR27","doi-asserted-by":"crossref","unstructured":"Tai Y, Yang J, Liu X (2017) Image super-resolution via deep recursive residual network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3147\u20133155","DOI":"10.1109\/CVPR.2017.298"},{"key":"2246_CR28","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2246_CR29","doi-asserted-by":"crossref","unstructured":"Tong T, Li G, Liu X et al (2017) Image super-resolution using dense skip connections. In: Proceedings of the IEEE international conference on computer vision, pp 4799\u20134807","DOI":"10.1109\/ICCV.2017.514"},{"key":"2246_CR30","unstructured":"Jaderberg M, Simonyan K, Zisserman A (2015) Spatial transformer networks. In: Advances in neural information processing systems, pp 2017\u20132025"},{"key":"2246_CR31","doi-asserted-by":"crossref","unstructured":"Jeon Y, Kim J (2017) Active convolution: learning the shape of convolution for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 4201\u20134209","DOI":"10.1109\/CVPR.2017.200"},{"key":"2246_CR32","doi-asserted-by":"crossref","unstructured":"Dai J, Qi H, Xiong Y et al (2017) Deformable convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp 764\u2013773","DOI":"10.1109\/ICCV.2017.89"},{"issue":"12","key":"2246_CR33","doi-asserted-by":"publisher","first-page":"1312","DOI":"10.3390\/rs9121312","volume":"9","author":"Z Xu","year":"2017","unstructured":"Xu Z, Xu X, Wang L, et al. (2017) Deformable convnet with aspect ratio constrained nms for object detection in remote sensing imagery. Remote Sensing 9(12):1312","journal-title":"Remote Sensing"},{"issue":"9","key":"2246_CR34","doi-asserted-by":"publisher","first-page":"1470","DOI":"10.3390\/rs10091470","volume":"10","author":"Y Ren","year":"2018","unstructured":"Ren Y, Zhu C, Xiao S (2018) Deformable faster r-cnn with aggregating multi-layer features for partially occluded object detection in optical remote sensing images. Remote Sens 10(9):1470","journal-title":"Remote Sens"},{"key":"2246_CR35","doi-asserted-by":"crossref","unstructured":"Ouyang W, Wang X, Zeng X et al (2015) Deepid-net: deformable deep convolutional neural networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2403\u20132412","DOI":"10.1109\/CVPR.2015.7298854"},{"key":"2246_CR36","doi-asserted-by":"crossref","unstructured":"Zhang M, Li X, Xu M et al (2018) RBC semantic segmentation for sickle cell disease based on deformable U-Net. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 695\u2013702","DOI":"10.1007\/978-3-030-00937-3_79"},{"key":"2246_CR37","doi-asserted-by":"publisher","first-page":"75530","DOI":"10.1109\/ACCESS.2019.2918800","volume":"7","author":"M Sun","year":"2019","unstructured":"Sun M, Zhang G, Dang H et al (2019) Accurate gastric cancer segmentation in digital pathology images using deformable convolution and multi-scale embedding networks. IEEE Access 7:75530\u201375541","journal-title":"IEEE Access"},{"key":"2246_CR38","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.knosys.2019.04.025","volume":"178","author":"Q Jin","year":"2019","unstructured":"Jin Q, Meng Z, Pham TD et al (2019) DUNet: a deformable network for retinal vessel segmentation. Knowledge-Based Systems 178:149\u2013162","journal-title":"Knowledge-Based Systems"},{"issue":"8","key":"2246_CR39","doi-asserted-by":"publisher","first-page":"1254","DOI":"10.1109\/LGRS.2018.2830403","volume":"15","author":"J Zhu","year":"2018","unstructured":"Zhu J, Fang L, Ghamisi P (2018) Deformable convolutional neural networks for hyperspectral image classification. IEEE Geosci Remote Sens Lett 15(8):1254\u20131258","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"2246_CR40","doi-asserted-by":"crossref","unstructured":"Timofte R, Agustsson E, Van Gool L et al (2017) Ntire 2017 challenge on single image super-resolution: methods and results. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 114\u2013125","DOI":"10.1109\/CVPRW.2017.150"},{"key":"2246_CR41","doi-asserted-by":"crossref","unstructured":"Bevilacqua M, Roumy A, Guillemot C et al (2012) Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: BMVC","DOI":"10.5244\/C.26.135"},{"key":"2246_CR42","doi-asserted-by":"crossref","unstructured":"Martin D, Fowlkes C, Tal D et al (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings eighth IEEE international conference on computer vision, pp 416\u2013423","DOI":"10.1109\/ICCV.2001.937655"},{"key":"2246_CR43","doi-asserted-by":"crossref","unstructured":"Huang JB, Singh A, Ahuja N (2015) Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5197\u20135206","DOI":"10.1109\/CVPR.2015.7299156"},{"issue":"4","key":"2246_CR44","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR et al (2004) Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4):600\u2013612","journal-title":"IEEE Transactions on Image Processing"},{"key":"2246_CR45","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980"},{"key":"2246_CR46","unstructured":"Paszke A, Gross S, Chintala S et al (2017) Automatic differentiation in pytorch"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02246-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02246-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02246-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T06:32:24Z","timestamp":1642141944000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02246-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,28]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["2246"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02246-0","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,28]]},"assertion":[{"value":"27 January 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}