{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T12:28:08Z","timestamp":1778502488437,"version":"3.51.4"},"reference-count":142,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976164"],"award-info":[{"award-number":["61976164"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s10462-022-10147-y","type":"journal-article","created":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T11:26:53Z","timestamp":1648812413000},"page":"5981-6035","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":178,"title":["Video super-resolution based on deep learning: a comprehensive survey"],"prefix":"10.1007","volume":"55","author":[{"given":"Hongying","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhubo","family":"Ruan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fanhua","family":"Shang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyuan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linlin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Radu","family":"Timofte","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,1]]},"reference":[{"issue":"3","key":"10147_CR1","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1109\/TPAMI.2019.2941941","volume":"43","author":"W Bao","year":"2021","unstructured":"Bao W, Lai W, Zhang X, Gao Z, Yang M (2021) MEMC-Net: motion estimation and motion compensation driven neural network for video interpolation and enhancement. IEEE Trans Pattern Anal Mach Intell 43(3):933\u2013948","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10147_CR2","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.neucom.2019.07.089","volume":"367","author":"B Bare","year":"2019","unstructured":"Bare B, Yan B, Ma C, Li K (2019) Real-time video super-resolution via motion convolution kernel estimation. Neurocomputing 367:236\u2013245","journal-title":"Neurocomputing"},{"key":"10147_CR3","doi-asserted-by":"crossref","unstructured":"Brox T, Bruhn A, Papenberg N, Weickert J (2004) High accuracy optical flow estimation based on a theory for warping. In: Pajdla T, Matas J (eds) European conference on computer vision, pp 25\u201336","DOI":"10.1007\/978-3-540-24673-2_3"},{"key":"10147_CR4","doi-asserted-by":"crossref","unstructured":"Burns C, Plyer A, Champagnat F (2017) Texture super-resolution for 3D reconstruction. In: Proceedings of the IAPR international conference on machine vision applications, pp 350\u2013353","DOI":"10.23919\/MVA.2017.7986873"},{"key":"10147_CR5","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":"10147_CR6","doi-asserted-by":"crossref","unstructured":"Chan KC, Wang X, Xu X, Gu J, Loy CC (2021a) GLEAN: generative latent bank for large-factor image super-resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14245\u201314254","DOI":"10.1109\/CVPR46437.2021.01402"},{"key":"10147_CR8","doi-asserted-by":"crossref","unstructured":"Chan KC, Wang X, Yu K, Dong C, Loy CC (2021b) BasicVSR: the search for essential components in video super-resolution and beyond. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4947\u20134956","DOI":"10.1109\/CVPR46437.2021.00491"},{"key":"10147_CR9","doi-asserted-by":"crossref","unstructured":"Chan KC, Wang X, Yu K, Dong C, Loy CC (2021c) Understanding deformable alignment in video super-resolution. Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 973\u2013981","DOI":"10.1609\/aaai.v35i2.16181"},{"key":"10147_CR7","doi-asserted-by":"crossref","unstructured":"Chan KCK, Zhou S, Xu X, Loy CC (2021d) BasicVSR++: improving video super-resolution with enhanced propagation and alignment. arXiv preprint arXiv:2104.13371","DOI":"10.1109\/CVPR52688.2022.00588"},{"key":"10147_CR11","unstructured":"Chen J, Tan X, Shan C, Liu S, Chen Z (2020) VESR-Net: the winning solution to Youku video enhancement and super-resolution challenge. arXiv preprint arXiv:2003.02115"},{"key":"10147_CR10","doi-asserted-by":"crossref","unstructured":"Chen Y, Liu S, Wang X (2021) Learning continuous image representation with local implicit image function. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8628\u20138638","DOI":"10.1109\/CVPR46437.2021.00852"},{"issue":"4","key":"10147_CR12","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1145\/3386569.3392457","volume":"39","author":"M Chu","year":"2020","unstructured":"Chu M, Xie Y, Mayer J, Leal-Taix\u00e9 L, Thuerey N (2020) Learning temporal coherence via self-supervision for GAN-based video generation. ACM Trans Graph 39(4):75","journal-title":"ACM Trans Graph"},{"key":"10147_CR15","doi-asserted-by":"crossref","unstructured":"Dai Q, Yoo S, Kappeler A, Katsaggelos AK (2015) Dictionary-based multiple frame video super-resolution. In: Proceedings of the IEEE international conference on image processing, pp 83\u201387","DOI":"10.1109\/ICIP.2015.7350764"},{"key":"10147_CR13","doi-asserted-by":"crossref","unstructured":"Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, Wei Y (2017) Deformable convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp 764\u2013773","DOI":"10.1109\/ICCV.2017.89"},{"key":"10147_CR14","doi-asserted-by":"crossref","unstructured":"Daithankar MV, Ruikar SD (2020) Video super resolution: a review. In: ICDSMLA 2019, pp 488\u2013495","DOI":"10.1007\/978-981-15-1420-3_51"},{"key":"10147_CR16","unstructured":"Dario F, Huang Z, Gu S, Radu T et\u00a0al (2020) Aim 2020 challenge on video extreme super-resolution: methods and results. arXiv preprint arXiv:2007.11803"},{"key":"10147_CR17","doi-asserted-by":"crossref","unstructured":"Dasari M, Bhattacharya A, Vargas S, Sahu P, Balasubramanian A, Das SR (2020) Streaming 360-degree videos using super-resolution. In: Proceedings of the IEEE conference on computer communications, pp 1977\u20131986","DOI":"10.1109\/INFOCOM41043.2020.9155477"},{"issue":"5","key":"10147_CR18","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1007\/s13042-017-0765-6","volume":"10","author":"AB Deshmukh","year":"2019","unstructured":"Deshmukh AB, Rani NU (2019) Fractional-grey wolf optimizer-based kernel weighted regression model for multi-view face video super resolution. Int J Mach Learn Cybern 10(5):859\u2013877","journal-title":"Int J Mach Learn Cybern"},{"key":"10147_CR19","doi-asserted-by":"crossref","unstructured":"Dong C, Loy CC, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution. In: European conference on computer vision, pp 184\u2013199","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"10147_CR20","doi-asserted-by":"crossref","unstructured":"Dong C, Loy CC, Tang X (2016) Accelerating the super-resolution convolutional neural network. In: European conference on computer vision, pp 391\u2013407","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"10147_CR21","doi-asserted-by":"crossref","unstructured":"Dosovitskiy A, Fischer P, Ilg E, Hausser P, Hazirbas C, Golkov V, Van Der Smagt P, Cremers D, Brox T (2015) FlowNet: learning optical flow with convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp 2758\u20132766","DOI":"10.1109\/ICCV.2015.316"},{"key":"10147_CR22","doi-asserted-by":"crossref","unstructured":"Drulea M, Nedevschi S (2011) Total variation regularization of local-global optical flow. In: 2011 14th IEEE international conference on intelligent transportation systems (ITSC), pp 318\u2013323","DOI":"10.1109\/ITSC.2011.6082986"},{"key":"10147_CR23","doi-asserted-by":"crossref","unstructured":"Fakour-Sevom V, Guldogan E, K\u00e4m\u00e4r\u00e4inen JK (2018) 360 panorama super-resolution using deep convolutional networks. In: International conference on computer vision theory and applications (VISAPP), pp 159\u2013165","DOI":"10.5220\/0006618901590165"},{"issue":"10","key":"10147_CR24","doi-asserted-by":"publisher","first-page":"1327","DOI":"10.1109\/TIP.2004.834669","volume":"13","author":"S Farsiu","year":"2004","unstructured":"Farsiu S, Robinson MD, Elad M, Milanfar P (2004) Fast and robust multiframe super resolution. IEEE Trans Image Process 13(10):1327\u20131344","journal-title":"IEEE Trans Image Process"},{"key":"10147_CR25","doi-asserted-by":"crossref","unstructured":"Fuoli D, Gu S, Timofte R (2019a) Efficient video super-resolution through recurrent latent space propagation. In: Proceedings of the IEEE\/CVF international conference on computer vision workshop, pp 3476\u20133485","DOI":"10.1109\/ICCVW.2019.00431"},{"key":"10147_CR26","doi-asserted-by":"crossref","unstructured":"Fuoli D, Gu S, Timofte R, Tao X, Li W, Guo T, Deng Z, Lu L, Dai T, Shen X et\u00a0al (2019b) Aim 2019 challenge on video extreme super-resolution: methods and results. In: Proceedings of the IEEE\/CVF international conference on computer vision workshop, pp 3467\u20133475","DOI":"10.1109\/ICCVW.2019.00430"},{"key":"10147_CR27","doi-asserted-by":"crossref","unstructured":"Fuoli D, Huang Z, Gu S, Timofte R, Raventos A, Esfandiari A, Karout S, Xu X, Li X, Xiong X et\u00a0al (2020) Aim 2020 challenge on video extreme super-resolution: methods and results. In: European conference on computer vision, pp 57\u201381","DOI":"10.1007\/978-3-030-66823-5_4"},{"key":"10147_CR28","doi-asserted-by":"crossref","unstructured":"Gautam A, Singh S (2020) A comparative analysis of deep learning based super-resolution techniques for thermal videos. In: Proceedings of the international conference on smart systems and inventive technology, pp 919\u2013925","DOI":"10.1109\/ICSSIT48917.2020.9214230"},{"key":"10147_CR29","doi-asserted-by":"crossref","unstructured":"Glaister J, Chan C, Frankovich M, Tang A, Wong A (2011) Hybrid video compression using selective keyframe identification and patch-based super-resolution. In: Proceedings of the IEEE international symposium on multimedia, pp 105\u2013110","DOI":"10.1109\/ISM.2011.25"},{"key":"10147_CR30","unstructured":"Gu J, Cai H, Chen H, Ye X, Ren J, Dong C (2020) Image quality assessment for perceptual image restoration: a new dataset, benchmark and metric. arXiv preprint arXiv:2011.15002"},{"key":"10147_CR32","doi-asserted-by":"crossref","unstructured":"Guo J, Chao H (2017) Building an end-to-end spatial-temporal convolutional network for video super-resolution. In: Proceedings of the AAAI conference on artificial intelligence, pp 4053\u20134060","DOI":"10.1609\/aaai.v31i1.11228"},{"key":"10147_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102691","volume":"166","author":"K Guo","year":"2020","unstructured":"Guo K, Guo H, Ren S, Zhang J, Li X (2020) Towards efficient motion-blurred public security video super-resolution based on back-projection networks. J Netw Comput Appl 166:102691","journal-title":"J Netw Comput Appl"},{"key":"10147_CR33","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":"10147_CR34","doi-asserted-by":"crossref","unstructured":"Haris M, 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":"10147_CR35","doi-asserted-by":"crossref","unstructured":"Haris M, Shakhnarovich G, Ukita N (2020) Space-time-aware multi-resolution video enhancement. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2859\u20132868","DOI":"10.1109\/CVPR42600.2020.00293"},{"key":"10147_CR37","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (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":"10147_CR36","first-page":"1","volume":"19","author":"Z He","year":"2020","unstructured":"He Z, He D, Li X, Xu J (2020) Unsupervised video satellite super-resolution by using only a single video. IEEE Geosci Remote Sens Lett 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"8","key":"10147_CR38","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"10147_CR45","doi-asserted-by":"crossref","unstructured":"Hu X, Mu H, Zhang X, Wang Z, Tan T, Sun J (2019) Meta-SR: a magnification-arbitrary network for super-resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1575\u20131584","DOI":"10.1109\/CVPR.2019.00167"},{"key":"10147_CR39","first-page":"235","volume":"28","author":"Y Huang","year":"2015","unstructured":"Huang Y, Wang W, Wang L (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":"10147_CR41","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"issue":"4","key":"10147_CR40","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1109\/TPAMI.2017.2701380","volume":"40","author":"Y Huang","year":"2018","unstructured":"Huang Y, Wang W, Wang L (2018) Video super-resolution via bidirectional recurrent convolutional networks. IEEE Trans Pattern Anal Mach Intell 40(4):1015\u20131028","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10147_CR44","doi-asserted-by":"crossref","unstructured":"Hui T, Tang X, Loy CC (2018) LiteFlowNet: a lightweight convolutional neural network for optical flow estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8981\u20138989","DOI":"10.1109\/CVPR.2018.00936"},{"issue":"8","key":"10147_CR42","doi-asserted-by":"publisher","first-page":"2555","DOI":"10.1109\/TPAMI.2020.2976928","volume":"43","author":"T Hui","year":"2021","unstructured":"Hui T, Tang X, Loy CC (2021a) A lightweight optical flow CNN-revisiting data fidelity and regularization. IEEE Trans Pattern Anal Mach Intell 43(8):2555\u20132569","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10147_CR43","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1016\/j.ins.2020.08.114","volume":"546","author":"Z Hui","year":"2021","unstructured":"Hui Z, Li J, Gao X, Wang X (2021b) Progressive perception-oriented network for single image super-resolution. Inf Sci 546:769\u2013786","journal-title":"Inf Sci"},{"key":"10147_CR46","doi-asserted-by":"crossref","unstructured":"Ignatov A, Romero A, Kim H, Timofte R et\u00a0al (2021) Real-time video super-resolution on smartphones with deep learning, mobile AI 2021 challenge: report. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops, pp 2535\u20132544","DOI":"10.1109\/CVPRW53098.2021.00287"},{"key":"10147_CR47","doi-asserted-by":"crossref","unstructured":"Ilg E, Mayer N, Saikia T, Keuper M, Dosovitskiy A, Brox T (2017) FlowNet 2.0: evolution of optical flow estimation with deep networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1647\u20131655","DOI":"10.1109\/CVPR.2017.179"},{"issue":"3","key":"10147_CR48","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/1049-9652(91)90045-L","volume":"53","author":"M Irani","year":"1991","unstructured":"Irani M, Peleg S (1991) Improving resolution by image registration. CVGIP Graph Models Image Process 53(3):231\u2013239","journal-title":"CVGIP Graph Models Image Process"},{"issue":"4","key":"10147_CR49","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1006\/jvci.1993.1030","volume":"4","author":"M Irani","year":"1993","unstructured":"Irani M, Peleg S (1993) Motion analysis for image enhancement: resolution, occlusion, and transparency. J Vis Commun Image Represent 4(4):324\u2013335","journal-title":"J Vis Commun Image Represent"},{"key":"10147_CR50","doi-asserted-by":"crossref","unstructured":"Isobe T, Jia X, Gu S, Li S, Wang S, Tian Q (2020) Video super-resolution with recurrent structure-detail network. In: European conference on computer vision, pp 645\u2013660","DOI":"10.1007\/978-3-030-58610-2_38"},{"key":"10147_CR51","unstructured":"Jacobsen JH, Smeulders AW, Oyallon E (2018) i-RevNet: deep invertible networks. In: Proceedings of the international conference on learning representations"},{"key":"10147_CR52","first-page":"2017","volume":"28","author":"M Jaderberg","year":"2015","unstructured":"Jaderberg M, Simonyan K, Zisserman A, Kavukcuoglu K (2015) Spatial transformer networks. Adv Neural Inf Process Syst 28:2017\u20132025","journal-title":"Adv Neural Inf Process Syst"},{"issue":"1","key":"10147_CR53","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","volume":"35","author":"S Ji","year":"2013","unstructured":"Ji S, Xu W, Yang M, Yu K (2013) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221\u2013231","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10147_CR54","first-page":"667","volume":"29","author":"X Jia","year":"2016","unstructured":"Jia X, De Brabandere B, Tuytelaars T, Gool LV (2016) Dynamic filter networks. Adv Neural Inf Process Syst 29:667\u2013675","journal-title":"Adv Neural Inf Process Syst"},{"issue":"11","key":"10147_CR55","doi-asserted-by":"publisher","first-page":"1630","DOI":"10.1109\/LSP.2018.2870536","volume":"25","author":"K Jiang","year":"2018","unstructured":"Jiang K, Wang Z, Yi P, Jiang J (2018a) A progressively enhanced network for video satellite imagery superresolution. IEEE Signal Process Lett 25(11):1630\u20131634","journal-title":"IEEE Signal Process Lett"},{"issue":"11","key":"10147_CR56","doi-asserted-by":"publisher","first-page":"1700","DOI":"10.3390\/rs10111700","volume":"10","author":"K Jiang","year":"2018","unstructured":"Jiang K, Wang Z, Yi P, Jiang J, Xiao J, Yao Y (2018b) Deep distillation recursive network for remote sensing imagery super-resolution. Remote Sens 10(11):1700","journal-title":"Remote Sens"},{"key":"10147_CR57","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"},{"key":"10147_CR58","doi-asserted-by":"crossref","unstructured":"Kalarot R, Porikli F (2019) MultiBoot VSR: multi-stage multi-reference bootstrapping for video super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 2060\u20132069","DOI":"10.1109\/CVPRW.2019.00258"},{"issue":"2","key":"10147_CR59","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1109\/TCI.2016.2532323","volume":"2","author":"A Kappeler","year":"2016","unstructured":"Kappeler A, Yoo S, Dai Q, Katsaggelos AK (2016) Video super-resolution with convolutional neural networks. IEEE Trans Comput Imaging 2(2):109\u2013122","journal-title":"IEEE Trans Comput Imaging"},{"key":"10147_CR64","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":"10147_CR61","doi-asserted-by":"crossref","unstructured":"Kim TH, Sajjadi MSM, Hirsch M, Sch\u00f6lkopf B (2018a) Spatio-temporal transformer network for video restoration. In: European conference on computer vision, pp 111\u2013127","DOI":"10.1007\/978-3-030-01219-9_7"},{"issue":"9","key":"10147_CR62","doi-asserted-by":"publisher","first-page":"1274","DOI":"10.1109\/TCSII.2018.2799577","volume":"65","author":"Y Kim","year":"2018","unstructured":"Kim Y, Choi JS, Kim M (2018b) 2x super-resolution hardware using edge-orientation-based linear mapping for real-time 4K UHD 60 fps video applications. IEEE Trans Circuits Syst Express Briefs 65(9):1274\u20131278","journal-title":"IEEE Trans Circuits Syst Express Briefs"},{"issue":"8","key":"10147_CR63","doi-asserted-by":"publisher","first-page":"2521","DOI":"10.1109\/TCSVT.2018.2864321","volume":"29","author":"Y Kim","year":"2018","unstructured":"Kim Y, Choi JS, Kim M (2018c) A real-time convolutional neural network for super-resolution on FPGA with applications to 4K UHD 60 fps video services. IEEE Trans Circuits Syst Video Technol 29(8):2521\u20132534","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"10147_CR60","doi-asserted-by":"crossref","unstructured":"Kim SY, Lim J, Na T, Kim M (2019) Video super-resolution based on 3D-CNNS with consideration of scene change. In: Proceedings of the IEEE international conference on image processing, pp 2831\u20132835","DOI":"10.1109\/ICIP.2019.8803297"},{"key":"10147_CR65","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"},{"issue":"20","key":"10147_CR66","doi-asserted-by":"publisher","first-page":"4405","DOI":"10.3390\/app9204405","volume":"9","author":"A Kwasniewska","year":"2019","unstructured":"Kwasniewska A, Ruminski J, Szankin M (2019) Improving accuracy of contactless respiratory rate estimation by enhancing thermal sequences with deep neural networks. Appl Sci 9(20):4405","journal-title":"Appl Sci"},{"key":"10147_CR67","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, Huszr 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":"10147_CR68","doi-asserted-by":"crossref","unstructured":"Lee Y, Yun J, Hong Y, Lee J, Jeon M (2018) Accurate license plate recognition and super-resolution using a generative adversarial networks on traffic surveillance video. In: Proceedings of the IEEE international conference on consumer electronics-Asia, ICCE-Asia, pp 1\u20134","DOI":"10.1109\/ICCE-ASIA.2018.8552121"},{"key":"10147_CR69","doi-asserted-by":"crossref","unstructured":"Lei P, Todorovic S (2018) Temporal deformable residual networks for action segmentation in videos. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6742\u20136751","DOI":"10.1109\/CVPR.2018.00705"},{"key":"10147_CR74","doi-asserted-by":"crossref","unstructured":"Li Y, Li X, Fu Z, Zhong W (2016) Multiview video super-resolution via information extraction and merging. In: Proceedings of the 24th ACM international conference on multimedia, pp 446\u2013450","DOI":"10.1145\/2964284.2967260"},{"key":"10147_CR72","doi-asserted-by":"crossref","unstructured":"Li K, Bare B, Yan B, Feng B, Yao C (2018) Face hallucination based on key parts enhancement. In: Proceedings of the IEEE international conference on acoustics, speech, and signal processing, pp 1378\u20131382","DOI":"10.1109\/ICASSP.2018.8462170"},{"issue":"3","key":"10147_CR70","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1109\/TIP.2018.2877334","volume":"28","author":"D Li","year":"2019","unstructured":"Li D, Liu Y, Wang Z (2019a) Video super-resolution using non-simultaneous fully recurrent convolutional network. IEEE Trans Image Process 28(3):1342\u20131355","journal-title":"IEEE Trans Image Process"},{"key":"10147_CR73","doi-asserted-by":"crossref","unstructured":"Li S, He F, Du B, Zhang L, Xu Y, Tao D (2019b) Fast spatio-temporal residual network for video super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 10522\u201310531","DOI":"10.1109\/CVPR.2019.01077"},{"key":"10147_CR78","doi-asserted-by":"crossref","unstructured":"Li Y, Tsiminaki V, Timofte R, Pollefeys M, Gool LV (2019c) 3D appearance super-resolution with deep learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9671\u20139680","DOI":"10.1109\/CVPR.2019.00990"},{"key":"10147_CR77","doi-asserted-by":"crossref","unstructured":"Li W, Tao X, Guo T, Qi L, Lu J, Jia J (2020) MuCAN: multi-correspondence aggregation network for video super-resolution. In: European conference on computer vision, pp 335\u2013351","DOI":"10.1007\/978-3-030-58607-2_20"},{"key":"10147_CR71","doi-asserted-by":"crossref","unstructured":"Liao R, Tao X, Li R, Ma Z, Jia J (2015) Video super-resolution via deep draft-ensemble learning. In: Proceedings of the IEEE international conference on computer vision, pp 531\u2013539","DOI":"10.1109\/ICCV.2015.68"},{"key":"10147_CR75","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H, Nah S, Lee KM (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 1132\u20131140","DOI":"10.1109\/CVPRW.2017.151"},{"key":"10147_CR76","doi-asserted-by":"crossref","unstructured":"Lin JY, Chang YC, Hsu WH (2020) Efficient and phase-aware video super-resolution for cardiac MRI. In: International conference on medical image computing and computer-assisted intervention (MICCAI), pp 66\u201376","DOI":"10.1007\/978-3-030-59719-1_7"},{"issue":"2","key":"10147_CR79","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1109\/TPAMI.2013.127","volume":"36","author":"C Liu","year":"2014","unstructured":"Liu C, Sun D (2014) 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":"10147_CR82","doi-asserted-by":"crossref","unstructured":"Liu Z, Cui C (2018) A new low bit-rate coding scheme for ultra high definition video based on super-resolution reconstruction. In: Proceedings of the IEEE international conference on computer and communication technology, pp 325\u2013329","DOI":"10.1109\/CCET.2018.8542304"},{"key":"10147_CR84","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 international conference on computer vision, pp 2526\u20132534","DOI":"10.1109\/ICCV.2017.274"},{"issue":"12","key":"10147_CR80","doi-asserted-by":"publisher","first-page":"8372","DOI":"10.1109\/TGRS.2020.2987400","volume":"58","author":"H Liu","year":"2020","unstructured":"Liu H, Gu Y, Wang T, Li S (2020a) Satellite video super-resolution based on adaptively spatiotemporal neighbors and nonlocal similarity regularization. IEEE Trans Geosci Remote Sens 58(12):8372\u20138383","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"10147_CR83","unstructured":"Liu H, Ruan Z, Fang C, Zhao P, Shang F, Liu Y, Wang L (2020b) A single frame and multi-frame joint network for 360-degree panorama video super-resolution. arXiv preprint arXiv:2008.10320"},{"key":"10147_CR81","doi-asserted-by":"publisher","first-page":"2127","DOI":"10.1109\/TIP.2021.3049974","volume":"30","author":"X Liu","year":"2021","unstructured":"Liu X, Shi K, Wang Z, Chen J (2021a) Exploit camera raw data for video super-resolution via hidden Markov model inference. IEEE Trans Image Process 30:2127\u20132140","journal-title":"IEEE Trans Image Process"},{"key":"10147_CR85","doi-asserted-by":"crossref","unstructured":"Liu H, Zhao P, Ruan Z, Shang F, Liu Y (2021b) Large motion video super-resolution with dual subnet and multi-stage communicated upsampling. In: Proceedings of the AAAI conference on artificial intelligence, pp 2127\u20132135","DOI":"10.1609\/aaai.v35i3.16310"},{"key":"10147_CR86","unstructured":"Loshchilov I, Hutter F (2017) SGDR: stochastic gradient descent with warm restarts. In: Proceedings of the international conference on learning representations (ICLR)"},{"key":"10147_CR87","unstructured":"Lucas BD, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: Proceedings of the international joint conference on artificial intelligence, pp 674\u2013679"},{"issue":"7","key":"10147_CR88","doi-asserted-by":"publisher","first-page":"3312","DOI":"10.1109\/TIP.2019.2895768","volume":"28","author":"A Lucas","year":"2019","unstructured":"Lucas A, Lopez-Tapia S, Molina R, Katsaggelos AK (2019) Generative adversarial networks and perceptual losses for video super-resolution. IEEE Trans Image Process 28(7):3312\u20133327","journal-title":"IEEE Trans Image Process"},{"issue":"12","key":"10147_CR89","doi-asserted-by":"publisher","first-page":"2398","DOI":"10.1109\/LGRS.2017.2766204","volume":"14","author":"Y Luo","year":"2017","unstructured":"Luo Y, Zhou L, Wang S, Wang Z (2017) Video satellite imagery super resolution via convolutional neural networks. IEEE Geosci Remote Sens Lett 14(12):2398\u20132402","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"10147_CR90","unstructured":"Ma Z, Liao R, Tao X, Xu L, Jia J, Wu E (2015) Handling motion blur in multi-frame super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5224\u20135232"},{"key":"10147_CR91","doi-asserted-by":"crossref","unstructured":"Nah S, Baik S, Hong S, Moon G, Son S, Timofte R, Lee KM (2019a) NTIRE 2019 challenge on video deblurring and super-resolution: dataset and study. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 1996\u20132005","DOI":"10.1109\/CVPRW.2019.00251"},{"key":"10147_CR92","doi-asserted-by":"crossref","unstructured":"Nah S, Timofte R, Gu S, Baik S, Hong S et\u00a0al (2019b) NTIRE 2019 challenge on video super-resolution: methods and results. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 1985\u20131995","DOI":"10.1109\/CVPRW.2019.00251"},{"key":"10147_CR93","doi-asserted-by":"crossref","unstructured":"Odille F, Bustin A, Chen B, Vuissoz PA, Felblinger J (2015) Motion-corrected, super-resolution reconstruction for high-resolution 3D cardiac cine MRI. In: International conference on medical image computing and computer-assisted intervention (MICCAI), pp 435\u2013442","DOI":"10.1007\/978-3-319-24574-4_52"},{"key":"10147_CR94","doi-asserted-by":"crossref","unstructured":"Pan J, Cheng S, Zhang J, Tang J (2020) Deep blind video super-resolution. arXiv preprint arXiv:2003.04716","DOI":"10.1109\/ICCV48922.2021.00477"},{"issue":"8","key":"10147_CR95","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1109\/83.605404","volume":"6","author":"AJ Patti","year":"1997","unstructured":"Patti AJ, Sezan MI, Tekalp AM (1997) Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time. IEEE Trans Image Process 6(8):1064\u20131076","journal-title":"IEEE Trans Image Process"},{"key":"10147_CR96","doi-asserted-by":"crossref","unstructured":"Peng C, Lin WA, Liao H, Chellappa R, Zhou SK (2020) SAINT: spatially aware interpolation network for medical slice synthesis. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7750\u20137759","DOI":"10.1109\/CVPR42600.2020.00777"},{"key":"10147_CR97","doi-asserted-by":"crossref","unstructured":"Poot DH, Van\u00a0Meir V, Sijbers J (2010) General and efficient super-resolution method for multi-slice MRI. In: International conference on medical image computing and computer-assisted intervention (MICCAI), pp 615\u2013622","DOI":"10.1007\/978-3-642-15705-9_75"},{"issue":"1","key":"10147_CR98","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1109\/TIP.2008.2008067","volume":"18","author":"M Protter","year":"2009","unstructured":"Protter M, Elad M, Takeda H, Milanfar P (2009) Generalizing the nonlocal-means to super-resolution reconstruction. IEEE Trans Image Process 18(1):36\u201351","journal-title":"IEEE Trans Image Process"},{"key":"10147_CR99","doi-asserted-by":"crossref","unstructured":"Ranjan A, Black MJ (2017) Optical flow estimation using a spatial pyramid network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2720\u20132729","DOI":"10.1109\/CVPR.2017.291"},{"key":"10147_CR100","doi-asserted-by":"crossref","unstructured":"Ren S, Guo H, Guo K (2019) Towards efficient medical video super-resolution based on deep back-projection networks. In: Proceedings of the IEEE international conference on iThings\/GreenCom\/CPSCom\/SmartData, pp 682\u2013686","DOI":"10.1109\/iThings\/GreenCom\/CPSCom\/SmartData.2019.00130"},{"key":"10147_CR101","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention (MICCAI), pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10147_CR102","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"},{"issue":"6","key":"10147_CR103","doi-asserted-by":"publisher","first-page":"996","DOI":"10.1109\/83.503915","volume":"5","author":"RR Schultz","year":"1996","unstructured":"Schultz RR, Stevenson RL (1996) Extraction of high-resolution frames from video sequences. IEEE Trans Image Process 5(6):996\u20131011","journal-title":"IEEE Trans Image Process"},{"issue":"17","key":"10147_CR104","doi-asserted-by":"publisher","first-page":"23815","DOI":"10.1007\/s11042-018-5915-7","volume":"78","author":"P Shamsolmoali","year":"2019","unstructured":"Shamsolmoali P, Zareapoor M, Jain DK, Jain VK, Yang J (2019) Deep convolution network for surveillance records super-resolution. Multimedia Tools Appl 78(17):23815\u201323829","journal-title":"Multimedia Tools Appl"},{"key":"10147_CR105","first-page":"802","volume":"28","author":"X Shi","year":"2015","unstructured":"Shi X, Chen Z, Wang H, Yeung DY, Wong Wk, Woo Wc (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting. Adv Neural Inf Process Syst 28:802\u2013810","journal-title":"Adv Neural Inf Process Syst"},{"key":"10147_CR106","doi-asserted-by":"crossref","unstructured":"Shi W, Caballero J, Huszr 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":"10147_CR107","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\/CVF conference on computer vision and pattern recognition, pp 3118\u20133126","DOI":"10.1109\/CVPR.2018.00329"},{"issue":"3","key":"10147_CR108","doi-asserted-by":"publisher","first-page":"1641","DOI":"10.1007\/s11042-019-08254-0","volume":"79","author":"A Singh","year":"2020","unstructured":"Singh A, Singh J (2020) Survey on single image based super-resolution-implementation challenges and solutions. Multimed Tools Appl 79(3):1641\u20131672","journal-title":"Multimed Tools Appl"},{"key":"10147_CR109","doi-asserted-by":"crossref","unstructured":"Son S, Lee S, Nah S, Timofte R, Lee KM et\u00a0al (2021) Ntire 2021 challenge on video super-resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition Workshops, pp 166\u2013181","DOI":"10.1109\/CVPRW53098.2021.00026"},{"key":"10147_CR111","doi-asserted-by":"crossref","unstructured":"Sun D, Yang X, Liu M, Kautz J (2018) PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8934\u20138943","DOI":"10.1109\/CVPR.2018.00931"},{"key":"10147_CR110","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2020.04.039","volume":"403","author":"W Sun","year":"2020","unstructured":"Sun W, Sun J, Zhu Y, Zhang Y (2020) Video super-resolution via dense non-local spatial-temporal convolutional network. Neurocomputing 403:1\u201312","journal-title":"Neurocomputing"},{"issue":"9","key":"10147_CR112","doi-asserted-by":"publisher","first-page":"1958","DOI":"10.1109\/TIP.2009.2023703","volume":"18","author":"H Takeda","year":"2009","unstructured":"Takeda H, Milanfar P, Protter M, Elad M (2009) Super-resolution without explicit subpixel motion estimation. IEEE Trans Image Process 18(9):1958\u20131975","journal-title":"IEEE Trans Image Process"},{"key":"10147_CR113","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 international conference on computer vision, pp 4482\u20134490","DOI":"10.1109\/ICCV.2017.479"},{"key":"10147_CR114","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 3360\u20133369","DOI":"10.1109\/CVPR42600.2020.00342"},{"key":"10147_CR115","doi-asserted-by":"crossref","unstructured":"Timofte R, De\u00a0Smet V, Van\u00a0Gool L (2014) A+: adjusted anchored neighborhood regression for fast super-resolution. In: Proceedings of the Asian conference on computer vision, pp 111\u2013126","DOI":"10.1007\/978-3-319-16817-3_8"},{"key":"10147_CR116","doi-asserted-by":"crossref","unstructured":"Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp 4489\u20134497","DOI":"10.1109\/ICCV.2015.510"},{"key":"10147_CR117","doi-asserted-by":"crossref","unstructured":"Umeda S, Yano N, Watanabe H, Ikai T, Chujoh T, Ito N (2018) HDR video super-resolution for future video coding. In: International workshop on advanced image technology, pp 1\u20134","DOI":"10.1109\/IWAIT.2018.8369700"},{"key":"10147_CR122","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"key":"10147_CR118","doi-asserted-by":"publisher","first-page":"177734","DOI":"10.1109\/ACCESS.2019.2958030","volume":"7","author":"H Wang","year":"2019","unstructured":"Wang H, Su D, Liu C, Jin L, Sun X, Peng X (2019a) Deformable non-local network for video super-resolution. IEEE Access 7:177734\u2013177744","journal-title":"IEEE Access"},{"issue":"5","key":"10147_CR119","doi-asserted-by":"publisher","first-page":"2530","DOI":"10.1109\/TIP.2018.2887017","volume":"28","author":"Z Wang","year":"2019","unstructured":"Wang Z, Yi P, Jiang K, Jiang J, Han Z, Lu T, Ma J (2019b) Multi-memory convolutional neural network for video super-resolution. IEEE Trans Image Process 28(5):2530\u20132544","journal-title":"IEEE Trans Image Process"},{"key":"10147_CR121","doi-asserted-by":"crossref","unstructured":"Wang X, Chan KCK, Yu K, Dong C, Loy CC (2019c) 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":"10147_CR123","doi-asserted-by":"crossref","unstructured":"Wang L, Guo Y, Lin Z, Deng X, An W (2019d) Learning for video super-resolution through HR optical flow estimation. In: Proceedings of the Asian conference on computer vision, pp 514\u2013529","DOI":"10.1007\/978-3-030-20887-5_32"},{"issue":"10","key":"10147_CR120","doi-asserted-by":"publisher","first-page":"3365","DOI":"10.1109\/TPAMI.2020.2982166","volume":"43","author":"Z Wang","year":"2021","unstructured":"Wang Z, Chen J, Hoi SC (2021a) Deep learning for image super-resolution: a survey. IEEE Trans Pattern Anal Mach Intell 43(10):3365\u20133387","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10147_CR124","doi-asserted-by":"crossref","unstructured":"Wang L, Wang Y, Lin Z, Yang J, An W, Guo Y (2021b) Learning a single network for scale-arbitrary super-resolution. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 4801\u20134810","DOI":"10.1109\/ICCV48922.2021.00476"},{"key":"10147_CR125","doi-asserted-by":"crossref","unstructured":"Wei Y, Chen L, Xie R, Song L, Zhang X, Gao Z (2019) FPGA based video transcoding system with 2K-4k super-resolution conversion. In: Proceedings of the IEEE international conference on visual communications and image processing, pp 1\u20132","DOI":"10.1109\/VCIP47243.2019.8966063"},{"issue":"4","key":"10147_CR126","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.3390\/s18041194","volume":"18","author":"A Xiao","year":"2018","unstructured":"Xiao A, Wang Z, Wang L, Ren Y (2018) Super-resolution for Jilin-1 satellite video imagery via a convolutional network. Sensors 18(4):1194","journal-title":"Sensors"},{"key":"10147_CR127","doi-asserted-by":"crossref","unstructured":"Xiao Z, Fu X, Huang J, Cheng Z, Xiong Z (2021) Space-time distillation for video super-resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2113\u20132122","DOI":"10.1109\/CVPR46437.2021.00215"},{"issue":"7","key":"10147_CR128","first-page":"12468","volume":"34","author":"J Xin","year":"2020","unstructured":"Xin J, Wang N, Li J, Gao X, Li Z (2020) Video face super-resolution with motion-adaptive feedback cell. Proc AAAI Conf Artif Intell 34(7):12468\u201312475","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"9","key":"10147_CR129","doi-asserted-by":"publisher","first-page":"1744","DOI":"10.1109\/TPAMI.2011.236","volume":"34","author":"L Xu","year":"2012","unstructured":"Xu L, Jia J, Matsushita Y (2012) Motion detail preserving optical flow estimation. IEEE Trans Pattern Anal Mach Intell 34(9):1744\u20131757","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"8","key":"10147_CR130","doi-asserted-by":"publisher","first-page":"1106","DOI":"10.1007\/s11263-018-01144-2","volume":"127","author":"T Xue","year":"2019","unstructured":"Xue T, Chen B, Wu J, Wei D, Freeman WT (2019) Video enhancement with task-oriented flow. Int J Comput Vis 127(8):1106\u20131125","journal-title":"Int J Comput Vis"},{"key":"10147_CR132","doi-asserted-by":"crossref","unstructured":"Yan B, Lin C, Tan W (2019) Frame and feature-context video super-resolution. In: Proceedings of the AAAI conference on artificial intelligence, pp 5597\u20135604","DOI":"10.1609\/aaai.v33i01.33015597"},{"issue":"12","key":"10147_CR131","doi-asserted-by":"publisher","first-page":"3106","DOI":"10.1109\/TMM.2019.2919431","volume":"21","author":"W Yang","year":"2019","unstructured":"Yang W, Zhang X, Tian Y, Wang W, Xue JH, Liao Q (2019) Deep learning for single image super-resolution: a brief review. IEEE Trans Multimedia 21(12):3106\u20133121","journal-title":"IEEE Trans Multimedia"},{"key":"10147_CR134","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 international conference on computer vision, pp 3106\u20133115","DOI":"10.1109\/ICCV.2019.00320"},{"key":"10147_CR133","doi-asserted-by":"crossref","unstructured":"Ying X, Wang L, Wang Y, Sheng W, An W, Guo Y (2020) Deformable 3D convolution for video super-resolution. arXiv preprint arXiv:200402803","DOI":"10.1109\/LSP.2020.3013518"},{"key":"10147_CR135","doi-asserted-by":"crossref","unstructured":"Yu H, Liu D, Shi H, Yu H, Wang Z, Wang X, Cross B, Bramler M, Huang TS (2017) Computed tomography super-resolution using convolutional neural networks. In: Proceedings of the IEEE international conference on image processing, pp 3944\u20133948","DOI":"10.1109\/ICIP.2017.8297022"},{"key":"10147_CR140","doi-asserted-by":"crossref","unstructured":"Zhang Y, Wu G, Yap PT, Feng Q, Lian J, Chen W, Shen D (2012) Reconstruction of super-resolution lung 4D-CT using patch-based sparse representation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 925\u2013931","DOI":"10.1109\/CVPR.2012.6247767"},{"key":"10147_CR136","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.image.2018.07.002","volume":"68","author":"T Zhang","year":"2018","unstructured":"Zhang T, Gao K, Ni G, Fan G, Lu Y (2018a) Spatio-temporal super-resolution for multi-videos based on belief propagation. Signal Process Image Commun 68:1\u201312","journal-title":"Signal Process Image Commun"},{"key":"10147_CR138","doi-asserted-by":"crossref","unstructured":"Zhang Y, Li K, Li K, Wang L, Zhong B, Fu Y (2018b) Image super-resolution using very deep residual channel attention networks. In: European conference on computer vision, pp 294\u2013310","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"10147_CR139","doi-asserted-by":"crossref","unstructured":"Zhang Y, Tian Y, Kong Y, Zhong B, Fu Y (2018c) Residual dense network for image super-resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2472\u20132481","DOI":"10.1109\/CVPR.2018.00262"},{"key":"10147_CR137","doi-asserted-by":"publisher","first-page":"5633","DOI":"10.1007\/s10462-021-09967-1","volume":"54","author":"W Zhang","year":"2021","unstructured":"Zhang W, Li H, Li Y, Liu H, Chen Y, Ding X (2021) Application of deep learning algorithms in geotechnical engineering: a short critical review. Artif Intell Rev 54:5633\u20135673","journal-title":"Artif Intell Rev"},{"key":"10147_CR141","doi-asserted-by":"crossref","unstructured":"Zhu X, Hu H, Lin S, Dai J (2019a) Deformable ConvNets V2: more deformable, better results. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9300\u20139308","DOI":"10.1109\/CVPR.2019.00953"},{"key":"10147_CR142","doi-asserted-by":"crossref","unstructured":"Zhu X, Li Z, Zhang X, Li C, Liu Y, Xue Z (2019b) Residual invertible spatio-temporal network for video super-resolution. In: Proceedings of the AAAI conference on artificial intelligence, pp 5981\u20135988","DOI":"10.1609\/aaai.v33i01.33015981"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-022-10147-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-022-10147-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-022-10147-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T18:08:44Z","timestamp":1668276524000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-022-10147-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,1]]},"references-count":142,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["10147"],"URL":"https:\/\/doi.org\/10.1007\/s10462-022-10147-y","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,1]]},"assertion":[{"value":"1 April 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}