{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T17:06:04Z","timestamp":1746810364563,"version":"3.37.3"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T00:00:00Z","timestamp":1664236800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T00:00:00Z","timestamp":1664236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100010909","name":"Young Scientists Fund","doi-asserted-by":"publisher","award":["61601404"],"award-info":[{"award-number":["61601404"]}],"id":[{"id":"10.13039\/501100010909","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s11042-022-13818-8","type":"journal-article","created":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T08:02:46Z","timestamp":1664265766000},"page":"14019-14035","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DRN-VideoSR: a deep recursive network for video super-resolution based on a deformable convolution shared-assignment network"],"prefix":"10.1007","volume":"82","author":[{"given":"Shaoshuo","family":"Mu","sequence":"first","affiliation":[]},{"given":"Yanhua","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yanbing","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,27]]},"reference":[{"key":"13818_CR1","doi-asserted-by":"publisher","first-page":"5403","DOI":"10.1007\/s11042-020-09824-3","volume":"80","author":"MY Abbass","year":"2021","unstructured":"Abbass MY, Kwon KC, Alam MS, Piao YL, Lee KY, Kim N (2021) Image super resolution based on residual dense CNN and guided filters. Multimed Tools Appl 80:5403\u20135421","journal-title":"Multimed Tools Appl"},{"key":"13818_CR2","doi-asserted-by":"crossref","unstructured":"Ahn N, Kang B, Sohn K (2018) Photo-realistic image super-resolution with fast and lightweight cascading residual network. The European Conference on Computer Vision (ECCV), pp 252\u2013268","DOI":"10.1109\/CVPRW.2018.00123"},{"key":"13818_CR3","unstructured":"Arjovsky M, Chintala S, Bottou L (2018) Wasserstein GAN. arXiv:1701.07875"},{"key":"13818_CR4","unstructured":"Berthelot D, Schumm T, Metz L (2017) BEGAN: boundary equilibrium generative adversarial networks. arXiv preprint arXiv:1703. 10717"},{"key":"13818_CR5","unstructured":"Bin H, Chen WH, Wu XM (2017) High- quality face image super resolution using conditional generative adversarial networks. arXiv preprint arXiv:1707.00737"},{"key":"13818_CR6","doi-asserted-by":"crossref","unstructured":"Caballero J, Ledig C, Aitken A et al (2017) Real-time video super resolution with spatio-temporal networks and motion compensation. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp 4778\u20134787","DOI":"10.1109\/CVPR.2017.304"},{"key":"13818_CR7","unstructured":"Chu M, Xie Y, Laura LT (2019) Temporally Coherent GANs for Video Super-Resolution(TecoGAN). arXiv:1811.09393"},{"key":"13818_CR8","doi-asserted-by":"crossref","unstructured":"Dai J, Qi H, Xiong Y et al (2017) Deformable convolutional networks. In: IEEE International Conference on Computer Vision (ICCV), pp 764\u2013773","DOI":"10.1109\/ICCV.2017.89"},{"key":"13818_CR9","doi-asserted-by":"crossref","unstructured":"Dong C, Loy CC, He K et al (2014) Learning a deep convolutianal network for image super-resolution. In: European Conference on Computer Vision(ECCV), pp 184\u2013199","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"13818_CR10","doi-asserted-by":"publisher","first-page":"11423","DOI":"10.1007\/s11042-020-10337-2","volume":"80","author":"L Fu","year":"2021","unstructured":"Fu L, Sun X, Zhao Y, Chen RJ, Chen H, Zhao R (2021) Video super-resolution reconstruction method based on deep Back projection and motion feature fusion. Multimed Tools Appl 80:11423\u201311441","journal-title":"Multimed Tools Appl"},{"key":"13818_CR11","doi-asserted-by":"crossref","unstructured":"Haris M, Shakhnarovich G, Ukita N (2019) Recurrent back-projection network for video super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp 3897\u20133906","DOI":"10.1109\/CVPR.2019.00402"},{"issue":"12","key":"13818_CR12","doi-asserted-by":"publisher","first-page":"4323","DOI":"10.1109\/TPAMI.2020.3002836","volume":"43","author":"M Haris","year":"2021","unstructured":"Haris M, Shakhnarovich G, Ukita N (2021) Deep Back-ProjectiNetworks for single image super-resolution. IEEE Trans Pattern Anal Mach Intell 43(12):4323\u20134337","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"13818_CR13","doi-asserted-by":"crossref","unstructured":"Hu XC, Mu HY, Zhang X et al (2019) Meta-SR: a magnification-arbitrary network for super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp 1575\u20131584","DOI":"10.1109\/CVPR.2019.00167"},{"key":"13818_CR14","first-page":"8005","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition(CVPR)","author":"T Isobe","year":"2020","unstructured":"Isobe T, Li SJ, Jia X et al (2020) Video super-resolution with temporal group attention. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp 8005\u20138014"},{"key":"13818_CR15","doi-asserted-by":"crossref","unstructured":"Isobe T, Jia X, Gu S (2020) Video super-resolution with recurrent structure- detail network. arXiv:2008.00455v1","DOI":"10.1007\/978-3-030-58610-2_38"},{"issue":"57","key":"13818_CR16","doi-asserted-by":"publisher","first-page":"5799","DOI":"10.1109\/TGRS.2019.2902431","volume":"8","author":"K Jiang","year":"2019","unstructured":"Jiang K, Wang Z, Yi P, Wang G, Lu T, Jiang J (2019) Edge-enhanced GAN for remote sensing image Superresolution. IEEE Trans Geosci Remote Sens 8(57):5799\u20135812","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"13818_CR17","doi-asserted-by":"publisher","first-page":"107475","DOI":"10.1016\/j.patcog.2020.107475","volume":"107","author":"K Jiang","year":"2020","unstructured":"Jiang K, Wang Z, Yi P (2020) Hierarchical dense recursive network for image super-resolution. Pattern Recognit 107:107475","journal-title":"Pattern Recognit"},{"key":"13818_CR18","doi-asserted-by":"crossref","unstructured":"Jo Y, Wug S, Kang J et al (2018) Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp 3224\u20133232","DOI":"10.1109\/CVPR.2018.00340"},{"key":"13818_CR19","doi-asserted-by":"crossref","unstructured":"Kim J, Lee JK, Lee KM (2016) Deeply-recursive convolutional network for image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp 1637\u20131645","DOI":"10.1109\/CVPR.2016.181"},{"key":"13818_CR20","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, Huszar F et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp 4681\u20134690","DOI":"10.1109\/CVPR.2017.19"},{"key":"13818_CR21","doi-asserted-by":"crossref","unstructured":"Li Z, Yang J, Liu Z et al (2019) Feedback network for image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp 3867\u20133876","DOI":"10.1109\/CVPR.2019.00399"},{"key":"13818_CR22","doi-asserted-by":"crossref","unstructured":"Li S, He FX, Du B et al (2019) Fast spatio-temporal residual network for video super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 10522\u201310533","DOI":"10.1109\/CVPR.2019.01077"},{"key":"13818_CR23","doi-asserted-by":"publisher","first-page":"4474","DOI":"10.1109\/TIP.2020.2972118","volume":"29","author":"F Li","year":"2020","unstructured":"Li F, Bai HH, Zhao Y (2020) Learning a deep dual attention network for video super-resolution. IEEE Trans Image Process 29:4474\u20134488","journal-title":"IEEE Trans Image Process"},{"key":"13818_CR24","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H et al (2017) Enhanced deep residual networks for single image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp 136\u2013144","DOI":"10.1109\/CVPRW.2017.151"},{"issue":"2","key":"13818_CR25","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1049\/iet-ipr.2010.0275","volume":"6","author":"A Maalouf","year":"2012","unstructured":"Maalouf A, Larabi M (2012) Colour image super-resolution using geometric grouplets. IET Image Process 6(2):168\u2013180","journal-title":"IET Image Process"},{"key":"13818_CR26","unstructured":"Mehdi SM, Vemulapalli R, Brown M (2018) Frame-recurrent video super- resolution. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp 6626\u20136634"},{"key":"13818_CR27","unstructured":"Min L, Yang P, Xu B et al (2019) Multi-image blind super-resolution in variational Bayesian framework. Opto-Electronic Engineering"},{"key":"13818_CR28","doi-asserted-by":"crossref","unstructured":"Shi W, Caballero J, Huszar F et al (2016) Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp 1874\u20131883","DOI":"10.1109\/CVPR.2016.207"},{"key":"13818_CR29","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.neucom.2020.03.068","volume":"406","author":"W Sun","year":"2020","unstructured":"Sun W, Zhang YN (2020) Attention-guided dual spatial-temporal non-local network for video super-resolution. Neurocomputing 406:24\u201333","journal-title":"Neurocomputing"},{"issue":"12","key":"13818_CR30","first-page":"1210004","volume":"37","author":"C Sun","year":"2017","unstructured":"Sun C, Lu J et al (2017) Method of rapid image super-resolution based on deconvolution. Acta Optica Sinica 37(12):1210004","journal-title":"Acta Optica Sinica"},{"key":"13818_CR31","doi-asserted-by":"crossref","unstructured":"Tai Y, Yang J, Liu X (2017) Image super-resolution via deep recursive residual network. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp 3147\u20133155","DOI":"10.1109\/CVPR.2017.298"},{"key":"13818_CR32","doi-asserted-by":"crossref","unstructured":"Tai Y, Yang J, Liu X (2017) Memnet: a persistent memory network for image restoration. In: IEEE International Conference on Computer Vision (ICCV), pp 4549\u20134557","DOI":"10.1109\/ICCV.2017.486"},{"key":"13818_CR33","doi-asserted-by":"crossref","unstructured":"Tian Y, Zhang Y, Fu Y, Xu C (2020) TDAN: Temporally-deformable alignment network for video super-resolution. 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 3357\u20133366","DOI":"10.1109\/CVPR42600.2020.00342"},{"key":"13818_CR34","doi-asserted-by":"crossref","unstructured":"Wang XT, Yu K, Dong C et al (2018) Recovering realistic texture in image super-resolution by deep spatial feature transform. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp 606\u2013615","DOI":"10.1109\/CVPR.2018.00070"},{"key":"13818_CR35","doi-asserted-by":"crossref","unstructured":"Wang XT, Yu K, Wu SX et al (2018) ESRGAN: enhanced super-resolution generative adversarial networks. The European Conference on Computer Vision (ECCV), pp 1\u201316","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"13818_CR36","doi-asserted-by":"crossref","unstructured":"Wang X, Chan KCK, Yu K et al (2019) EDVR: video restoration with enhanced deformable convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp 1954\u20131963","DOI":"10.1109\/CVPRW.2019.00247"},{"key":"13818_CR37","doi-asserted-by":"publisher","first-page":"4323","DOI":"10.1109\/TIP.2020.2967596","volume":"29","author":"L Wang","year":"2020","unstructured":"Wang L, Guo Y, Liu L, Lin Z, Deng X, An W (2020) Deep video super-resolution using HR optical flow estimation. IEEE Trans Image Process 29:4323\u20134336","journal-title":"IEEE Trans Image Process"},{"key":"13818_CR38","doi-asserted-by":"crossref","unstructured":"Wang S, Zhou T, Lu Y, Di H (2022) Detail-preserving transformer for light field image super-resolution. In: Association for the Advance of Artificial Intelligence (AAAI)","DOI":"10.1609\/aaai.v36i3.20153"},{"key":"13818_CR39","first-page":"1","volume":"60","author":"S Wang","year":"2022","unstructured":"Wang S, Zhou T, Lu Y, Di H (2022) Contextual transformation network for lightweight remote-sensing image super-resolution. IEEE Trans Geosci Remote Sens 60:1\u201313","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"13818_CR40","doi-asserted-by":"crossref","unstructured":"Yi P, Wang ZY, Jiang K et al (2019) Progressive fusion video superresolution network via exploiting non-local spatio-temporal correlations. In: IEEE International Conference on Computer Vision (ICCV), pp 3106\u20133115","DOI":"10.1109\/ICCV.2019.00320"},{"issue":"44","key":"13818_CR41","first-page":"2264","volume":"5","author":"P Yi","year":"2020","unstructured":"Yi P, Wang Z, Jiang K et al (2020) A progressive fusion generative adversarial network for realistic and consistent video super-resolution. IEEE Trans Pattern Anal Mach Intell 5(44):2264\u20132280","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"30","key":"13818_CR42","doi-asserted-by":"publisher","first-page":"2503","DOI":"10.1109\/TCSVT.2019.2925844","volume":"8","author":"P Yi","year":"2020","unstructured":"Yi P, Wang Z, Jiang K, Shao Z, Ma J (2020) Multi- temporal ultra dense memory network for video super-resolution. IEEE Trans Circuits Syst Video Technol 8(30):2503\u20132516","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"13818_CR43","doi-asserted-by":"crossref","unstructured":"Yoon Y, Jeon H, Yoo D et al (2015) Learning a deep convolutional network for light-field image super-resolution. In: IEEE International Conference on Computer Vision Workshop, vol 17, pp 57\u201365","DOI":"10.1109\/ICCVW.2015.17"},{"key":"13818_CR44","doi-asserted-by":"crossref","unstructured":"Zhang YL, Tian YP, Kong Y et al (2018) Residual dense network for image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp 2472\u20132481","DOI":"10.1109\/CVPR.2018.00262"},{"key":"13818_CR45","doi-asserted-by":"crossref","unstructured":"Zhang S, Lin Y, Sheng H (2019) Residual networks for light field image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp 11046\u201311055","DOI":"10.1109\/CVPR.2019.01130"},{"key":"13818_CR46","doi-asserted-by":"publisher","first-page":"8326","DOI":"10.1109\/TIP.2020.3013162","volume":"29","author":"T Zhou","year":"2020","unstructured":"Zhou T, Li J, Wang S, Tao R, Shen J (2020) MATNet: motion-attentive transition network for zero-shot video object segmentation. IEEE Trans Image Process 29:8326\u20138338","journal-title":"IEEE Trans Image Process"},{"key":"13818_CR47","doi-asserted-by":"crossref","unstructured":"Zhou T, Wang W, Liu S et al (2021) Differentiable multi-granularity human representation learning for instance-aware human semantic parsing. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1622\u20131631","DOI":"10.1109\/CVPR46437.2021.00167"},{"key":"13818_CR48","doi-asserted-by":"crossref","unstructured":"Zhou T, Li J, Li X, Shao L (2021) Target-aware object discovery and association for unsupervised video multi-object segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 6981\u20136990","DOI":"10.1109\/CVPR46437.2021.00691"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-13818-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-13818-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-13818-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T10:36:10Z","timestamp":1679394970000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-13818-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,27]]},"references-count":48,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["13818"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-13818-8","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2022,9,27]]},"assertion":[{"value":"4 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 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 author declares he has no confict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}