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In recent years, deep learning has made great progress in many fields such as image recognition, natural language processing, speech recognition and video super-resolution.
Wang X, Chan KCK, Yu K, Dong C, Loy CC (2019a) EDVR: Video restoration with enhanced deformable convolutional networks. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops, pp 1954–1963
1963
Earlier work this paper cites.
Takeda H, Milanfar P, Protter M, Elad M (2009) Super-resolution without explicit subpixel motion estimation. IEEE Trans Image Process 18(9):1958–1975
1975
Earlier work this paper cites.
Lucas BD, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: Proc. Int. Joint Conf. Artif. Intell., pp 674–679
1981
Earlier work this paper cites.
Dasari M, Bhattacharya A, Vargas S, Sahu P, Balasubramanian A, Das SR (2020) Streaming 360-degree videos using super-resolution. In: Proc. IEEE Conf. Comput. Commun., pp 1977–1986
1986
Earlier work this paper cites.
Irani M, Peleg S (1991) Improving resolution by image registration. CVGIP Graphical Models Image Process 53(3):231 – 239
1991
Earlier work this paper cites.
Irani M, Peleg S (1993) Motion analysis for image enhancement: Resolution, occlusion, and transparency. J Vis Commun Image Represent 4(4):324 – 335
1993
Earlier work this paper cites.
Nah S, Timofte R, Gu S, Baik S, Hong S, et al. (2019b) NTIRE 2019 challenge on video super-resolution: Methods and results. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops, pp 1985–1995
1995
Earlier work this paper cites.
Schultz RR, Stevenson RL (1996) Extraction of high-resolution frames from video sequences. IEEE Trans Image Process 5(6):996–1011
1996
Earlier work this paper cites.
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
1997
Earlier work this paper cites.
Patti AJ, Sezan MI, Tekalp AM (1997) Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time. IEEE Trans Image Process 6(8):1064–1076
1997
Earlier work this paper cites.
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) Eur. Conf. Comput. Vis., pp 25–36
2004
Earlier work this paper cites.
Farsiu S, Robinson MD, Elad M, Milanfar P (2004) Fast and robust multiframe super resolution. IEEE Trans Image Process 13(10):1327–1344
2004
Earlier work this paper cites.
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: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops, pp 1996–2005
2005
Earlier work this paper cites.
Protter M, Elad M, Takeda H, Milanfar P (2009) Generalizing the nonlocal-means to super-resolution reconstruction. IEEE Trans Image Process 18(1):36–51
2009
Earlier work this paper cites.
Poot DH, Van Meir V, Sijbers J (2010) General and efficient super-resolution method for multi-slice MRI. In: Med. Image Comput. Comput. Assist. Interv. (MICCAI), pp 615–622
2010
Earlier work this paper cites.
Drulea M, Nedevschi S (2011) Total variation regularization of local-global optical flow. In: 2011 14th Int. IEEE Conf. Intell. Transp. Syst. (ITSC), pp 318–323
2011
Earlier work this paper cites.
Glaister J, Chan C, Frankovich M, Tang A, Wong A (2011) Hybrid video compression using selective keyframe identification and patch-based super-resolution. In: Proc. IEEE Int. Symp. Multimedia, pp 105–110
2011
Earlier work this paper cites.
Xu L, Jia J, Matsushita Y (2012) Motion detail preserving optical flow estimation. IEEE Trans Pattern Anal Mach Intell 34(9):1744–1757
2012
Earlier work this paper cites.
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: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp 925–931
2012
Earlier work this paper cites.
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):221–231
2013
Earlier work this paper cites.
Dong C, Loy CC, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution. In: Eur. Conf. Comput. Vis., pp 184–199
2014
Earlier work this paper cites.
Liu C, Sun D (2014) On Bayesian adaptive video super resolution. IEEE Trans Pattern Anal Mach Intell 36(2):346–360
2014
Earlier work this paper cites.
Timofte R, De Smet V, Van Gool L (2014) A+: Adjusted anchored neighborhood regression for fast super-resolution. In: Proc. Asian Conf. Comput. Vis., pp 111–126
2014
Earlier work this paper cites.
Dai Q, Yoo S, Kappeler A, Katsaggelos AK (2015) Dictionary-based multiple frame video super-resolution. In: Proc. IEEE Int. Conf. Image Process., pp 83–87
2015
Earlier work this paper cites.
Dosovitskiy A, Fischer P, Ilg E, Husser P, Hazirbas C, Golkov V, v d Smagt P, Cremers D, Brox T (2015) FlowNet: Learning optical flow with convolutional networks. In: Proc. IEEE Int. Conf. Comput. Vis., pp 2758–2766
2015
Earlier work this paper cites.
Huang Y, Wang W, Wang L (2015) Bidirectional recurrent convolutional networks for multi-frame super-resolution. In: Adv. Neural Inf. Process. Syst., pp 235–243
2015
Earlier work this paper cites.
Liao R, Tao X, Li R, Ma Z, Jia J (2015) Video super-resolution via deep draft-ensemble learning. In: Proc IEEE Int. Conf. Comput. Vis., pp 531–539
2015
Earlier work this paper cites.
Ma Z, Liao R, Tao X, Xu L, Jia J, Wu E (2015) Handling motion blur in multi-frame super-resolution. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp 5224–5232
2015
Earlier work this paper cites.
Odille F, Bustin A, Chen B, Vuissoz PA, Felblinger J (2015) Motion-corrected, super-resolution reconstruction for high-resolution 3d cardiac cine MRI. In: Med. Image Comput. Comput. Assist. Interv. (MICCAI), pp 435–442
2015
Earlier work this paper cites.
Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Med. Image Comput. Comput. Assist. Interv. (MICCAI), pp 234–241
2015
Earlier work this paper cites.
Shi X, Chen Z, Wang H, Yeung DY, Wong Wk, Woo Wc (2015) Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In: Adv. Neural Inf. Process. Syst. 28, pp 802–810
2015
Earlier work this paper cites.
Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3d convolutional networks. In: Proc IEEE Int. Conf. Comput. Vis., pp 4489–4497
2015
Earlier work this paper cites.
Dong C, Loy CC, Tang X (2016) Accelerating the super-resolution convolutional neural network. In: Eur. Conf. Comput. Vis., pp 391–407
2016
Earlier work this paper cites.
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp 770–778
2016
Earlier work this paper cites.
Jia X, De Brabandere B, Tuytelaars T, Gool LV (2016) Dynamic filter networks. In: Adv. Neural Inf. Process. Syst. 29, pp 667–675
2016
Earlier work this paper cites.
Kappeler A, Yoo S, Dai Q, Katsaggelos AK (2016) Video super-resolution with convolutional neural networks. IEEE Trans Comput Imag 2(2):109–122
2016
Earlier work this paper cites.
Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp 1646–1654
2016
Earlier work this paper cites.
Li Y, Li X, Fu Z, Zhong W (2016) Multiview video super-resolution via information extraction and merging. In: Proc. 24th ACM Int. Conf. Multimedia, pp 446–450
2016
Earlier work this paper cites.
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: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp 1874–1883
2016
Earlier work this paper cites.
Burns C, Plyer A, Champagnat F (2017) Texture super-resolution for 3d reconstruction. In: Proc. IAPR Int. Conf. Mach. Vis. Appl., pp 350–353
2017
Earlier work this paper cites.
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: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp 2848–2857
2017
Earlier work this paper cites.
Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, Wei Y (2017) Deformable convolutional networks. In: Proc IEEE Int. Conf. Comput. Vis., pp 764–773
2017
Cited alongside, same era.
Guo J, Chao H (2017) Building an end-to-end spatial-temporal convolutional network for video super-resolution. In: Proc. AAAI Conf. Artif. Intell., pp 4053–4060
2017
Cited alongside, same era.
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp 2261–2269
2017
Cited alongside, same era.
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: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp 1647–1655
2017
Cited alongside, same era.
Ren S, Guo H, Guo K (2019) Towards efficient medical video super-resolution based on deep back-projection networks. In: Proc. IEEE Int. Conf. iThings/GreenCom/CPSCom/SmartData, pp 682–686
2019
Later among the works it cites.
Shamsolmoali P, Zareapoor M, Jain DK, Jain VK, Yang J (2019) Deep convolution network for surveillance records super-resolution. Multimed Tools Appl 78(17):23815–23829
2019
Later among the works it cites.
Wang H, Su D, Liu C, Jin L, Sun X, Peng X (2019) Deformable non-local network for video super-resolution. IEEE Access 7:177734–177744
2019
Later among the works it cites.
Wang L, Guo Y, Lin Z, Deng X, An W (2019) Learning for video super-resolution through HR optical flow estimation. In: Proc. Asian Conf. Comput. Vis., pp 514–529
2019
Later among the works it cites.
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: Proc. IEEE Int. Conf. Vis. Commun. Image Process., pp 1–2
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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: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp 105–114
2017
Cited alongside, same era.
Lim B, Son S, Kim H, Nah S, Lee KM (2017) Enhanced deep residual networks for single image super-resolution. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops, pp 1132–1140
2017
Cited alongside, same era.
Liu D, Wang Z, Fan Y, Liu X, Wang Z, Chang S, Huang T (2017) Robust video super-resolution with learned temporal dynamics. In: Proc IEEE Int. Conf. Comput. Vis., pp 2526–2534
2017
Cited alongside, same era.
Loshchilov I, Hutter F (2017) Sgdr: Stochastic gradient descent with warm restarts. In: Proc. Int. Conf. Learn. Represent. (ICLR)
2017
Cited alongside, same era.
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):2398–2402
2017
Cited alongside, same era.
Ranjan A, Black MJ (2017) Optical flow estimation using a spatial pyramid network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp 2720–2729
2017
Cited alongside, same era.
Tao X, Gao H, Liao R, Wang J, Jia J (2017) Detail-revealing deep video super-resolution. In: Proc IEEE Int. Conf. Comput. Vis., pp 4482–4490
2017
Cited alongside, same era.
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: Proc. IEEE Int. Conf. Image Process., pp 3944–3948
2017
Cited alongside, same era.
2019
Later among the works it cites.
Xue T, Chen B, Wu J, Wei D, Freeman WT (2019) Video enhancement with task-oriented flow. Int J Comput Vis 127(8):1106–1125
2019
Later among the works it cites.
Yan B, Lin C, Tan W (2019) Frame and feature-context video super-resolution. In: Proc. AAAI Conf. Artif. Intell., pp 5597–5604
2019
Later among the works it cites.
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):3106–3121
2019
Later among the works it cites.
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: Proc IEEE Int. Conf. Comput. Vis., pp 3106–3115
2019
Later among the works it cites.
Zhu X, Hu H, Lin S, Dai J (2019) Deformable ConvNets V2: More deformable, better results. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp 9300–9308
2019
Later among the works it cites.
Zhu X, Li Z, Zhang X, Li C, Liu Y, Xue Z (2019) Residual invertible spatio-temporal network for video super-resolution. In: Proc. AAAI Conf. Artif. Intell., pp 5981–5988
2019
Later among the works it cites.
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:200302115
2020
Closest in time.
Chu M, Xie Y, Mayer J, Leal-Taixé L, Thuerey N (2020) Learning temporal coherence via self-supervision for gan-based video generation. ACM Trans Graph 39(4):75
2020
Closest in time.
Dario F, Huang Z, Gu S, Radu T, et al. (2020) Aim 2020 challenge on video extreme super-resolution: Methods and results. arXiv preprint arXiv:200711803
2020
Closest in time.
Fuoli D, Huang Z, Gu S, Timofte R, Raventos A, Esfandiari A, Karout S, Xu X, Li X, Xiong X, et al. (2020) Aim 2020 challenge on video extreme super-resolution: Methods and results. In: Eur. Conf. Comput. Vis., pp 57–81
2020
Closest in time.
Gautam A, Singh S (2020) A comparative analysis of deep learning based super-resolution techniques for thermal videos. In: Proc. Int. Conf. Smart Syst. Inven. Technol., pp 919–925
2020
Closest in time.
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: 201115002
2020
Closest in time.
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
2020
Closest in time.
Haris M, Shakhnarovich G, Ukita N (2020) Space-time-aware multi-resolution video enhancement. In: Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pp 2859–2868
2020
Closest in time.
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
2020
Closest in time.
Isobe T, Jia X, Gu S, Li S, Wang S, Tian Q (2020) Video super-resolution with recurrent structure-detail network. In: Eur. Conf. Comput. Vis., pp 645–660
2020
Closest in time.
Li W, Tao X, Guo T, Qi L, Lu J, Jia J (2020) MuCAN: Multi-correspondence aggregation network for video super-resolution. In: Eur. Conf. Comput. Vis., pp 335–351
2020
Closest in time.
Lin JY, Chang YC, Hsu WH (2020) Efficient and phase-aware video super-resolution for cardiac mri. In: Med. Image Comput. Comput. Assist. Interv. (MICCAI), pp 66–76
2020
Closest in time.
Pan J, Cheng S, Zhang J, Tang J (2020) Deep blind video super-resolution. arXiv preprint arXiv:200304716
2020
Closest in time.
Peng C, Lin WA, Liao H, Chellappa R, Zhou SK (2020) Saint: Spatially aware interpolation network for medical slice synthesis. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp 7750–7759
2020
Closest in time.
Singh A, Singh J (2020) Survey on single image based super-resolution-implementation challenges and solutions. Multimed Tools Appl 79(3):1641–1672
2020
Closest in time.
Sun W, Sun J, Zhu Y, Zhang Y (2020) Video super-resolution via dense non-local spatial-temporal convolutional network. Neurocomputing 403:1–12
2020
Closest in time.
Tian Y, Zhang Y, Fu Y, Xu C (2020) TDAN: Temporally-deformable alignment network for video super-resolution. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp 3360–3369
2020
Closest in time.
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):12468–12475
2020
Closest in time.
Ying X, Wang L, Wang Y, Sheng W, An W, Guo Y (2020) Deformable 3d convolution for video super-resolution. arXiv preprint arXiv:200402803
2020
Closest in time.
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):933–948
2021
Closest in time.
Chen Y, Liu S, Wang X (2021) Learning continuous image representation with local implicit image function. In: Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pp 8628–8638
2021
Closest in time.
Hui T, Tang X, Loy CC (2021) A lightweight optical flow cnn - revisiting data fidelity and regularization. IEEE Trans Pattern Anal Mach Intell 43(8):2555–2569
2021
Closest in time.
Hui Z, Li J, Gao X, Wang X (2021) Progressive perception-oriented network for single image super-resolution. Information Sciences 546:769–786
2021
Closest in time.
Ignatov A, Romero A, Kim H, Timofte R, et al. (2021) Real-time video super-resolution on smartphones with deep learning, mobile ai 2021 challenge: Report. In: Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Workshops, pp 2535–2544
2021
Closest in time.
Son S, Lee S, Nah S, Timofte R, Lee KM, et al. (2021) Ntire 2021 challenge on video super-resolution. In: Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Workshops, pp 166–181
2021
Closest in time.
Xiao Z, Fu X, Huang J, Cheng Z, Xiong Z (2021) Space-time distillation for video super-resolution. In: Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pp 2113–2122
2021
Closest in time.
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 Review pp 1–41
2021
Closest in time.
Jaderberg M, Simonyan K, Zisserman A, kavukcuoglu k (2015) Spatial transformer networks. In: Adv. Neural Inf. Process. Syst. 28, pp 2017–2025
2025
Closest in time.
Kalarot R, Porikli F (2019) MultiBoot VSR: Multi-stage multi-reference bootstrapping for video super-resolution. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops, pp 2060–2069
2069
Closest in time.