{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:38:55Z","timestamp":1740123535360,"version":"3.37.3"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T00:00:00Z","timestamp":1642377600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T00:00:00Z","timestamp":1642377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100014718","name":"innovative research group project of the national natural science foundation of china","doi-asserted-by":"publisher","award":["61703196"],"award-info":[{"award-number":["61703196"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011457","name":"state key laboratory of soil plant machinery system technology","doi-asserted-by":"publisher","award":["ZZ2019ZD11","ZZ2021J23"],"award-info":[{"award-number":["ZZ2019ZD11","ZZ2021J23"]}],"id":[{"id":"10.13039\/501100011457","id-type":"DOI","asserted-by":"publisher"}]},{"name":"fujian province nature science foundation","award":["2020J01813","2020J01821"],"award-info":[{"award-number":["2020J01813","2020J01821"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2022,5]]},"DOI":"10.1007\/s11227-021-04299-x","type":"journal-article","created":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T12:02:53Z","timestamp":1642420973000},"page":"8999-9016","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deeply feature fused video super-resolution network using temporal grouping"],"prefix":"10.1007","volume":"78","author":[{"given":"Zhensen","family":"Chen","sequence":"first","affiliation":[]},{"given":"Wenyuan","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6467-7545","authenticated-orcid":false,"given":"Jingmin","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,17]]},"reference":[{"key":"4299_CR1","doi-asserted-by":"crossref","unstructured":"Ahn N, Kang B, Sohn K-A (2018) Fast, accurate, and lightweight super-resolution with cascading residual network. In: Proceedings of the European Conference on Computer Vision, pp 252\u2013268","DOI":"10.1109\/CVPRW.2018.00123"},{"key":"4299_CR2","doi-asserted-by":"crossref","unstructured":"Bertasius G, Torresani L, Shi J (2018) Object detection in video with spatiotemporal sampling networks. In: Proceedings of the European Conference on Computer Vision, pp 331\u2013346","DOI":"10.1007\/978-3-030-01258-8_21"},{"key":"4299_CR3","doi-asserted-by":"crossref","unstructured":"Caballero J, Ledig C, Aitken A, Acosta A, Totz J, Wang Z, Shi W (2017) Real-time video super-resolution with spatio-temporal networks and motion compensation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2848\u20132857","DOI":"10.1109\/CVPR.2017.304"},{"key":"4299_CR4","doi-asserted-by":"crossref","unstructured":"Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: In Proceedings of the European Conference on Computer Vision, pp 801\u2013818","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"4299_CR5","doi-asserted-by":"crossref","unstructured":"Dai Jifeng, Qi Haozhi, Xiong Yuwen, Li Yi, Zhang Guodong, Hu Han, Wei Yichen (2017) Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp 764\u2013773","DOI":"10.1109\/ICCV.2017.89"},{"key":"4299_CR6","doi-asserted-by":"crossref","unstructured":"Dong Chao, Loy Chen\u00a0Change, He Kaiming, Tang Xiaoou (2014) Learning a deep convolutional network for image super-resolution. In: Proceedings of the European Conference on Computer Vision, pp 184\u2013199","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"4299_CR7","doi-asserted-by":"crossref","unstructured":"Gast J, Roth S (2019) Deep video deblurring: the devil is in the details. In: Proceedings of the IEEE\/CVF Conference on International Conference on Computer Vision Workshop, pp 3824\u20133833","DOI":"10.1109\/ICCVW.2019.00475"},{"key":"4299_CR8","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Proceedings of the International Conference on Artificial Intelligence and Statistics, pp 315\u2013323"},{"key":"4299_CR9","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":"4299_CR10","doi-asserted-by":"crossref","unstructured":"Haris Muhammad, Shakhnarovich G, Ukita N (2019) Recurrent back-projection network for video super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3892\u20133901","DOI":"10.1109\/CVPR.2019.00402"},{"key":"4299_CR11","doi-asserted-by":"crossref","unstructured":"Huang X, Belongie S (2017) Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE Conference on International Conference on Computer Vision, pp 1510\u20131519","DOI":"10.1109\/ICCV.2017.167"},{"key":"4299_CR12","first-page":"235","volume":"28","author":"Yan Huang","year":"2015","unstructured":"Huang Yan, Wang Wei, Wang Liang (2015) Bidirectional recurrent convolutional networks for multi-frame super-resolution. Adv Neural Inf Process Syst 28:235\u2013243","journal-title":"Adv Neural Inf Process Syst"},{"key":"4299_CR13","doi-asserted-by":"publisher","first-page":"2325","DOI":"10.1109\/TIP.2021.3050856","volume":"30","author":"Yuanfei Huang","year":"2021","unstructured":"Huang Yuanfei, Li Jie, Gao Xinbo, Yanting Hu, Wen Lu (2021) Interpretable detail-fidelity attention network for single image super-resolution. IEEE Trans Image Process 30:2325\u20132339","journal-title":"IEEE Trans Image Process"},{"key":"4299_CR14","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp 448\u2013456"},{"key":"4299_CR15","doi-asserted-by":"crossref","unstructured":"Isobe T, Li S, Jia X, Yuan S, Slabaugh G, Xu C, Li Y-L, Wang S, Tian Q (2020) Video super-resolution with temporal group attention. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 8005\u20138014","DOI":"10.1109\/CVPR42600.2020.00803"},{"key":"4299_CR16","doi-asserted-by":"crossref","unstructured":"Jo Y, Oh SW, Kang J, Kim SJ (2018) Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3224\u20133232","DOI":"10.1109\/CVPR.2018.00340"},{"issue":"2","key":"4299_CR17","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1109\/TCI.2016.2532323","volume":"2","author":"Kappeler Armin","year":"2016","unstructured":"Armin Kappeler, Seunghwan Yoo, Qiqin Dai, Katsaggelos Aggelos K (2016) Video super-resolution with convolutional neural networks. IEEE Trans Comput Imag 2(2):109\u2013122","journal-title":"IEEE Trans Comput Imag"},{"key":"4299_CR18","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":"4299_CR19","unstructured":"Kim SY, Lim J, Na T, Kim M (2018) 3dsrnet: video super-resolution using 3d convolutional neural networks. CoRR, arXiv:abs\/1812.09079"},{"key":"4299_CR20","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980"},{"key":"4299_CR21","doi-asserted-by":"crossref","unstructured":"Kupyn O, Budzan V, Mykhailych M, Mishkin D, Matas J (2018) Deblurgan: blind motion deblurring using conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 8183\u20138192","DOI":"10.1109\/CVPR.2018.00854"},{"key":"4299_CR22","doi-asserted-by":"crossref","unstructured":"Lai W-S, Huang J-B, Ahuja N, Yang M-H (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":"4299_CR23","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, Husz\u00e1r F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z, Shi W (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 105\u2013114","DOI":"10.1109\/CVPR.2017.19"},{"key":"4299_CR24","doi-asserted-by":"crossref","unstructured":"Li Z, Yang J, Liu Z, Yang X, Jeon G, Wu W (2019) Feedback network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3867\u20133876","DOI":"10.1109\/CVPR.2019.00399"},{"key":"4299_CR25","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H, Nah S, Mu\u00a0LK (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"},{"issue":"2","key":"4299_CR26","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1109\/TPAMI.2013.127","volume":"36","author":"Ce Liu","year":"2013","unstructured":"Liu Ce, Sun Deqing (2013) On Bayesian adaptive video super resolution. IEEE Trans Pattern Anal Mach Intell 36(2):346\u2013360","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4299_CR27","doi-asserted-by":"crossref","unstructured":"Liu D, Wang Z, Fan Y, Liu X, Wang Z, Chang S, Huang T (2017) Robust video super-resolution with learned temporal dynamics. In: Proceedings of the IEEE Conference on International Conference on Computer Vision, pp 2526\u20132534","DOI":"10.1109\/ICCV.2017.274"},{"key":"4299_CR28","doi-asserted-by":"crossref","unstructured":"Nah S, Tae HK, Kyoung ML (2017) Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3883\u20133891","DOI":"10.1109\/CVPR.2017.35"},{"issue":"2","key":"4299_CR29","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1007\/s11227-019-03066-3","volume":"76","author":"Y Qi","year":"2020","unstructured":"Qi Y, Junhua G, Li W, Tian Z, Zhang Y, Geng J (2020) Pulmonary nodule image super-resolution using multi-scale deep residual channel attention network with joint optimization. J Supercomput 76(2):1005\u20131019","journal-title":"J Supercomput"},{"key":"4299_CR30","doi-asserted-by":"crossref","unstructured":"Ren D, Zhang K, Wang Q, Hu Q, Zuo W (2020) Neural blind deconvolution using deep priors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3338\u20133347","DOI":"10.1109\/CVPR42600.2020.00340"},{"issue":"1","key":"4299_CR31","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1109\/TPAMI.2019.2926357","volume":"43","author":"Dongwei Ren","year":"2021","unstructured":"Ren Dongwei, Zuo Wangmeng, Zhang David, Zhang Lei, Yang Ming-Hsuan (2021) Simultaneous fidelity and regularization learning for image restoration. IEEE Trans Pattern Anal Mach Intell 43(1):284\u2013299","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4299_CR32","doi-asserted-by":"crossref","unstructured":"Sajjadi MSM, Vemulapalli R, Brown M (2018) Frame-recurrent video super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 6626\u20136634","DOI":"10.1109\/CVPR.2018.00693"},{"key":"4299_CR33","doi-asserted-by":"crossref","unstructured":"Shi W, Caballero J, Husz\u00e1r F, Totz J, Aitken AP, Bishop R, Rueckert D, Wang Z (2016) 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, pp 1874\u20131883","DOI":"10.1109\/CVPR.2016.207"},{"key":"4299_CR34","doi-asserted-by":"publisher","first-page":"2923","DOI":"10.1109\/TIP.2021.3056868","volume":"30","author":"Xu Song Huihui","year":"2021","unstructured":"Song Huihui Xu, Wenjie Liu Dong, Bo Liu, Qingshan Liu, Metaxas Dimitris N (2021) Multi-stage feature fusion network for video super-resolution. IEEE Trans Image Process 30:2923\u20132934","journal-title":"IEEE Trans Image Process"},{"key":"4299_CR35","doi-asserted-by":"crossref","unstructured":"Tao X, Gao H, Liao R, Wang J, Jia J (2017) Detail-revealing deep video super-resolution. In: Proceedings of the IEEE Conference on International Conference on Computer Vision, pp 4482\u20134490","DOI":"10.1109\/ICCV.2017.479"},{"key":"4299_CR36","doi-asserted-by":"crossref","unstructured":"Tian Y, Zhang Y, Fu Y, Xu C (2020) Tdan: temporally-deformable alignment network for video super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3357\u20133366","DOI":"10.1109\/CVPR42600.2020.00342"},{"issue":"4","key":"4299_CR37","doi-asserted-by":"publisher","first-page":"3594","DOI":"10.1007\/s11227-020-03407-7","volume":"77","author":"Tseng Kuo-Kun","year":"2021","unstructured":"Kuo-Kun Tseng, Ran Zhang, Chien-Ming Chen, Mehedi Hassan Mohammad (2021) Dnetunet: a semi-supervised CNN of medical image segmentation for super-computing ai service. J Supercomput 77(4):3594\u20133615","journal-title":"J Supercomput"},{"key":"4299_CR38","doi-asserted-by":"crossref","unstructured":"Wang L, Guo Y, Lin Z, Deng X, An W (2018) Learning for video super-resolution through hr optical flow estimation. In: Asian Conference on Computer Vision, pp 514\u2013529","DOI":"10.1007\/978-3-030-20887-5_32"},{"key":"4299_CR39","doi-asserted-by":"publisher","first-page":"4323","DOI":"10.1109\/TIP.2020.2967596","volume":"29","author":"Wang Longguang","year":"2020","unstructured":"Longguang Wang, Yulan Guo, Li Liu, Zaiping Lin, Xinpu Deng, Wei An (2020) Deep video super-resolution using hr optical flow estimation. IEEE Trans Image Process 29:4323\u20134336","journal-title":"IEEE Trans Image Process"},{"key":"4299_CR40","doi-asserted-by":"crossref","unstructured":"Wang X, Chan KCK, Yu K, Dong C, Loy CC (2019) Edvr: video restoration with enhanced deformable convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 1954\u20131963","DOI":"10.1109\/CVPRW.2019.00247"},{"key":"4299_CR41","doi-asserted-by":"crossref","unstructured":"Wang X, Yu K, Dong C, Loy CC (2018) Recovering realistic texture in image super-resolution by deep spatial feature transform. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 606\u2013615","DOI":"10.1109\/CVPR.2018.00070"},{"key":"4299_CR42","doi-asserted-by":"publisher","first-page":"6142","DOI":"10.1109\/TIP.2021.3092814","volume":"30","author":"Wen Yang","year":"2021","unstructured":"Yang Wen, Jie Chen, Bin Sheng, Zhihua Chen, Ping Li, Ping Tan, Tong-Yee Lee (2021) Structure-aware motion deblurring using multi-adversarial optimized cyclegan. IEEE Trans Image Process 30:6142\u20136155","journal-title":"IEEE Trans Image Process"},{"issue":"8","key":"4299_CR43","doi-asserted-by":"publisher","first-page":"1106","DOI":"10.1007\/s11263-018-01144-2","volume":"127","author":"Xue Tianfan","year":"2019","unstructured":"Tianfan Xue, Chen Baian Wu, Jiajun Wei Donglai, Freeman William T (2019) Video enhancement with task-oriented flow. Int J Comput Vis 127(8):1106\u20131125","journal-title":"Int J Comput Vis"},{"key":"4299_CR44","doi-asserted-by":"crossref","unstructured":"Yi P, Wang Z, Jiang K, Jiang J, Ma J (2019) Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations. In: Proceedings of the IEEE Conference on International Conference on Computer Vision, pp 3106\u20133115","DOI":"10.1109\/ICCV.2019.00320"},{"key":"4299_CR45","doi-asserted-by":"crossref","unstructured":"Zhang K, Zuo W, Zhang L (2019) Deep plug-and-play super-resolution for arbitrary blur kernels. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1671\u20131681","DOI":"10.1109\/CVPR.2019.00177"},{"key":"4299_CR46","doi-asserted-by":"crossref","unstructured":"Zhang Y, Li K, Li K, Wang L, Zhong B, Fu Y (2018) Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision, pp 286\u2013301","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"4299_CR47","doi-asserted-by":"crossref","unstructured":"Zhang Y, Tian Y, Kong Y, Zhong B, Fu Y (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":"4299_CR48","unstructured":"Zhao Y, Xiong Y, Lin D (2018) Trajectory convolution for action recognition. In: Proceedings of the International Conference on Neural Information Processing Systems, pp 2208\u20132219"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-021-04299-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-021-04299-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-021-04299-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T15:21:07Z","timestamp":1650381667000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-021-04299-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,17]]},"references-count":48,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,5]]}},"alternative-id":["4299"],"URL":"https:\/\/doi.org\/10.1007\/s11227-021-04299-x","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"type":"print","value":"0920-8542"},{"type":"electronic","value":"1573-0484"}],"subject":[],"published":{"date-parts":[[2022,1,17]]},"assertion":[{"value":"28 December 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}