{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T14:26:57Z","timestamp":1768141617675,"version":"3.49.0"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,1,8]],"date-time":"2023-01-08T00:00:00Z","timestamp":1673136000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,1,8]],"date-time":"2023-01-08T00:00:00Z","timestamp":1673136000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Swiss Federal Institute of Technology Zurich"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Video enhancement is a challenging problem, more than that of stills, mainly due to high computational cost, larger data volumes and the difficulty of achieving consistency in the spatio-temporal domain. In practice, these challenges are often coupled with the lack of example pairs, which inhibits the application of supervised learning strategies. To address these challenges, we propose an efficient adversarial video enhancement framework that learns directly from unpaired video examples. In particular, our framework introduces new recurrent cells that consist of interleaved local and global modules for implicit integration of spatial and temporal information. The proposed design allows our recurrent cells to efficiently propagate spatio-temporal information across frames and reduces the need for high complexity networks. Our setting enables learning from unpaired videos in a cyclic adversarial manner, where the proposed recurrent units are employed in all architectures. Efficient training is accomplished by introducing one single discriminator that learns the joint distribution of source and target domain simultaneously. The enhancement results demonstrate clear superiority of the proposed video enhancer over the state-of-the-art methods, in all terms of visual quality, quantitative metrics, and inference speed. Notably, our video enhancer is capable of enhancing over 35 frames per second of FullHD video (1080x1920).<\/jats:p>","DOI":"10.1007\/s11263-022-01735-0","type":"journal-article","created":{"date-parts":[[2023,1,8]],"date-time":"2023-01-08T10:03:36Z","timestamp":1673172216000},"page":"1042-1059","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An Efficient Recurrent Adversarial Framework for Unsupervised Real-Time Video Enhancement"],"prefix":"10.1007","volume":"131","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3168-4014","authenticated-orcid":false,"given":"Dario","family":"Fuoli","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiwu","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danda Pani","family":"Paudel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luc","family":"Van Gool","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":[[2023,1,8]]},"reference":[{"key":"1735_CR1","doi-asserted-by":"crossref","unstructured":"Aittala, M., & Durand, F. (2018). Burst image deblurring using permutation invariant convolutional neural networks. In Proceedings of the European conference on computer vision (ECCV) (pp. 731\u2013747).","DOI":"10.1007\/978-3-030-01237-3_45"},{"issue":"1","key":"1735_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11263-010-0390-2","volume":"92","author":"S Baker","year":"2011","unstructured":"Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011). A database and evaluation methodology for optical flow. International Journal of Computer Vision, 92(1), 1\u201331.","journal-title":"International Journal of Computer Vision"},{"key":"1735_CR3","doi-asserted-by":"crossref","unstructured":"Bansal, A., Ma, S., Ramanan, D., & Yaser, S. (2018). Recycle-gan: Unsupervised video retargeting. In ECCV.","DOI":"10.1007\/978-3-030-01228-1_8"},{"key":"1735_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Y., Pan, Y., Yao, T., Tian, X., & Mei, T. (2019). Mocycle-gan: Unpaired video-to-video translation. In Proceedings of the 27th ACM international conference on multimedia, MM \u201919, 647\u2013655, New York, NY, USA. Association for Computing Machinery.","DOI":"10.1145\/3343031.3350937"},{"key":"1735_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Y.-S., Wang, Y.-C., Kao, M.-H., & Chuang, Y.-Y. (2018). Deep photo enhancer: Unpaired learning for image enhancement from photographs with gans. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6306\u20136314).","DOI":"10.1109\/CVPR.2018.00660"},{"key":"1735_CR6","unstructured":"Chu, M., Xie, Y., Leal-Taix\u00e9, L., & Thuerey, N. (2018). Temporally coherent gans for video super-resolution (tecogan). arXiv preprint arXiv:1811.09393, 1(2), 3."},{"key":"1735_CR7","doi-asserted-by":"crossref","unstructured":"Dai, Y., Liu, D., & Wu, F. (2017). A convolutional neural network approach for post-processing in hevc intra coding. In International conference on multimedia modeling (pp. 28\u201339). Springer.","DOI":"10.1007\/978-3-319-51811-4_3"},{"key":"1735_CR8","doi-asserted-by":"crossref","unstructured":"Fuoli, D., Gu, S., & Timofte, R. (2019). Efficient video super-resolution through recurrent latent space propagation. In ICCV workshops.","DOI":"10.1109\/ICCVW.2019.00431"},{"key":"1735_CR9","doi-asserted-by":"crossref","unstructured":"Fuoli, D., Gu, S., & Timofte, R., et\u00a0al. (2019). Aim 2019 challenge on video extreme super-resolution: Methods and results. In ICCV workshops.","DOI":"10.1109\/ICCVW.2019.00430"},{"key":"1735_CR10","doi-asserted-by":"crossref","unstructured":"Fuoli, D., Huang, Z., Danelljan, M., Timofte, R. (2020). Ntire 2020 challenge on video quality mapping: Methods and results. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops (pp. 476\u2013477).","DOI":"10.1109\/CVPRW50498.2020.00246"},{"key":"1735_CR11","doi-asserted-by":"crossref","unstructured":"Fuoli, D., Huang, Z., Gu, S., Timofte, R., et\u00a0al. (2020). AIM 2020 challenge on video extreme super-resolution: Methods and results. In European conference on computer vision workshops.","DOI":"10.1007\/978-3-030-66823-5_4"},{"key":"1735_CR12","doi-asserted-by":"crossref","unstructured":"Galteri, L., Seidenari, L., Bertini, M., Uricchio, T., & Del\u00a0Bimbo, A. (2019). Fast video quality enhancement using gans. In Proceedings of the 27th ACM international conference on multimedia (pp. 1065\u20131067).","DOI":"10.1145\/3343031.3350592"},{"key":"1735_CR13","doi-asserted-by":"crossref","unstructured":"Gast, J., & Roth, S. (2019). Deep video deblurring: The devil is in the details. In Proceedings of the IEEE international conference on computer vision workshops.","DOI":"10.1109\/ICCVW.2019.00475"},{"issue":"8","key":"1735_CR14","doi-asserted-by":"publisher","first-page":"2445","DOI":"10.1016\/j.sigpro.2009.09.004","volume":"90","author":"M Ghoniem","year":"2010","unstructured":"Ghoniem, M., Chahir, Y., & Elmoataz, A. (2010). Nonlocal video denoising, simplification and inpainting using discrete regularization on graphs. Signal Processing, 90(8), 2445\u20132455.","journal-title":"Signal Processing"},{"key":"1735_CR15","doi-asserted-by":"crossref","unstructured":"Godard, C., Matzen, K., & Uyttendaele, M. (2018). Deep burst denoising. In Proceedings of the European conference on computer vision (ECCV) (pp. 538\u2013554).","DOI":"10.1007\/978-3-030-01267-0_33"},{"key":"1735_CR16","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672\u20132680)."},{"key":"1735_CR17","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A.C. (2017). Improved training of wasserstein gans. In I. Guyon, U.\u00a0V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett (eds.), Advances in neural information processing systems (Vol. 30, pp. 5767\u20135777). Curran Associates, Inc."},{"key":"1735_CR18","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017). Gans trained by a two time-scale update rule converge to a local nash equilibrium. In I. Guyon, U.\u00a0V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (eds.), Advances in neural information processing systems (Vol. 30, pp. 6626\u20136637). Curran Associates, Inc."},{"issue":"2","key":"1735_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3181974","volume":"37","author":"Y Hu","year":"2018","unstructured":"Hu, Y., He, H., Xu, C., Wang, B., & Lin, S. (2018). Exposure: A white-box photo post-processing framework. ACM Transactions on Graphics (TOG), 37(2), 1\u201317.","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"1735_CR20","doi-asserted-by":"crossref","unstructured":"Ignatov, A., Kobyshev, N., Timofte, R., Vanhoey, K., & Van\u00a0Gool, L. (2017). Dslr-quality photos on mobile devices with deep convolutional networks. In Proceedings of the IEEE international conference on computer vision (pp. 3277\u20133285).","DOI":"10.1109\/ICCV.2017.355"},{"key":"1735_CR21","doi-asserted-by":"crossref","unstructured":"Ignatov, A., Kobyshev, N., Timofte, R., Vanhoey, K., & Van\u00a0Gool, L. (2018). Wespe: Weakly supervised photo enhancer for digital cameras. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 691\u2013700).","DOI":"10.1109\/CVPRW.2018.00112"},{"issue":"4","key":"1735_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2897824.2925974","volume":"35","author":"S Iizuka","year":"2016","unstructured":"Iizuka, S., Simo-Serra, E., & Ishikawa, H. (2016). Let there be color! joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. ACM Transaction on Graphics, 35(4), 1\u201311.","journal-title":"ACM Transaction on Graphics"},{"key":"1735_CR23","doi-asserted-by":"crossref","unstructured":"Jiang, H., Sun, D., Jampani, V., Yang, M.-H., Learned-Miller, E., & Kautz, J. (2018). Super slomo: High quality estimation of multiple intermediate frames for video interpolation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 9000\u20139008).","DOI":"10.1109\/CVPR.2018.00938"},{"key":"1735_CR24","doi-asserted-by":"crossref","unstructured":"Jiang, X., Le\u00a0Pendu, M., & Guillemot, C. (2018). Depth estimation with occlusion handling from a sparse set of light field views. In 2018 25th IEEE international conference on image processing (ICIP) (pp. 634\u2013638). IEEE.","DOI":"10.1109\/ICIP.2018.8451466"},{"key":"1735_CR25","doi-asserted-by":"crossref","unstructured":"Jo, Y., Wug\u00a0Oh, S., Kang, J., & Joo\u00a0Kim, S. (2018). Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In The IEEE conference on computer vision and pattern recognition (CVPR), June.","DOI":"10.1109\/CVPR.2018.00340"},{"key":"1735_CR26","unstructured":"Jolicoeur-Martineau, A. (2018). The relativistic discriminator: A key element missing from standard gan. arXiv preprint arXiv:1807.00734."},{"key":"1735_CR27","doi-asserted-by":"crossref","unstructured":"Kim, S., Li, G., Fuoli, D., Danelljan, M., Huang, Z., Gu, S., & Timofte, R. (2019). The vid3oc and intvid datasets for video super resolution and quality mapping. In 2019 IEEE\/CVF international conference on computer vision workshop (ICCVW) (pp. 3609\u20133616). IEEE.","DOI":"10.1109\/ICCVW.2019.00446"},{"key":"1735_CR28","unstructured":"Kingma, D.P., & Ba, J. (2014). Adam: A method for stochastic optimization. CoRR, abs\/1412.6980."},{"key":"1735_CR29","doi-asserted-by":"crossref","unstructured":"Kosugi, S., & Yamasaki, T. (2019). Unpaired image enhancement featuring reinforcement-learning-controlled image editing software. arXiv preprint arXiv:1912.07833.","DOI":"10.1609\/aaai.v34i07.6790"},{"key":"1735_CR30","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Huszar, 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 (CVPR), July.","DOI":"10.1109\/CVPR.2017.19"},{"key":"1735_CR31","doi-asserted-by":"crossref","unstructured":"Liu, C., & Freeman, W.T. (2010). A high-quality video denoising algorithm based on reliable motion estimation. In European conference on computer vision (pp. 706\u2013719). Springer.","DOI":"10.1007\/978-3-642-15558-1_51"},{"key":"1735_CR32","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 The IEEE international conference on computer vision (ICCV).","DOI":"10.1109\/ICCV.2017.274"},{"key":"1735_CR33","doi-asserted-by":"crossref","unstructured":"Liu, X., Kong, L., Zhou, Y., Zhao, J., & Chen, J. (2020). End-to-end trainable video super-resolution based on a new mechanism for implicit motion estimation and compensation. In The IEEE winter conference on applications of computer vision (pp. 2416\u20132425).","DOI":"10.1109\/WACV45572.2020.9093552"},{"issue":"9","key":"1735_CR34","doi-asserted-by":"publisher","first-page":"3952","DOI":"10.1109\/TIP.2012.2199324","volume":"21","author":"M Maggioni","year":"2012","unstructured":"Maggioni, M., Boracchi, G., Foi, A., & Egiazarian, K. (2012). Video denoising, deblocking, and enhancement through separable 4-d nonlocal spatiotemporal transforms. IEEE Transactions on Image Processing, 21(9), 3952\u20133966.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1735_CR35","unstructured":"Mathieu, M., Couprie, C., & LeCun, Y. (2015). Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440."},{"key":"1735_CR36","doi-asserted-by":"crossref","unstructured":"Mildenhall, B., Barron, J.T, Chen, J., Sharlet, D., Ng, R., & Carroll, R. (2018). Burst denoising with kernel prediction networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2502\u20132510).","DOI":"10.1109\/CVPR.2018.00265"},{"key":"1735_CR37","unstructured":"Miyato, T., Kataoka, T., Koyama, M., & Yoshida, Y. (2018). Spectral normalization for generative adversarial networks. In International conference on learning representations."},{"issue":"6","key":"1735_CR38","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1007\/s00138-014-0623-4","volume":"25","author":"K Nasrollahi","year":"2014","unstructured":"Nasrollahi, K., & Moeslund, T. B. (2014). Super-resolution: A comprehensive survey. Machine Vision and Applications, 25(6), 1423\u20131468.","journal-title":"Machine Vision and Applications"},{"key":"1735_CR39","doi-asserted-by":"crossref","unstructured":"Ni, Z., Yang, W., Wang, S., Ma, L., & Kwong, S. (2020). Towards unsupervised deep image enhancement with generative adversarial network. IEEE Transactions on Image Processing.","DOI":"10.1145\/3394171.3413839"},{"key":"1735_CR40","doi-asserted-by":"crossref","unstructured":"Niklaus, S., & Liu, F. (2018). Context-aware synthesis for video frame interpolation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1701\u20131710).","DOI":"10.1109\/CVPR.2018.00183"},{"key":"1735_CR41","doi-asserted-by":"crossref","unstructured":"Niklaus, S., Mai, L., & Liu, F. (2017). Video frame interpolation via adaptive convolution. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 670\u2013679).","DOI":"10.1109\/CVPR.2017.244"},{"key":"1735_CR42","unstructured":"Obukhov, A., Seitzer, M., Wu, P.-W., Zhydenko, S., Kyl, J., & Lin, E. Y.-J. (2020). toshas\/torch-fidelity: Version 0.2.0, May."},{"key":"1735_CR43","doi-asserted-by":"crossref","unstructured":"Park, Jongchan, Lee, Joon-Young, Yoo, Donggeun, & So\u00a0Kweon, In. (2018). Distort-and-recover: Color enhancement using deep reinforcement learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5928\u20135936).","DOI":"10.1109\/CVPR.2018.00621"},{"key":"1735_CR44","doi-asserted-by":"crossref","unstructured":"Park, K., Woo, S., Kim, D., Cho, D., Kweon, I.\u00a0S. (2019). Preserving semantic and temporal consistency for unpaired video-to-video translation. In Proceedings of the 27th ACM international conference on multimedia, MM \u201919 (pp. 1248\u20131257). New York, NY, USA. Association for Computing Machinery.","DOI":"10.1145\/3343031.3350864"},{"key":"1735_CR45","doi-asserted-by":"crossref","unstructured":"Sajjadi, M.S.M., Vemulapalli, R., & Brown, M. (2018). Frame-recurrent video super-resolution. In The IEEE conference on computer vision and pattern recognition (CVPR), June.","DOI":"10.1109\/CVPR.2018.00693"},{"key":"1735_CR46","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4510\u20134520).","DOI":"10.1109\/CVPR.2018.00474"},{"key":"1735_CR47","unstructured":"Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In International conference on learning representations."},{"key":"1735_CR48","doi-asserted-by":"crossref","unstructured":"Su, S., Delbracio, M., Wang, J., Sapiro, G., Heidrich, W., & Wang, O. (2017). Deep video deblurring for hand-held cameras. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1279\u20131288).","DOI":"10.1109\/CVPR.2017.33"},{"key":"1735_CR49","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), June.","DOI":"10.1109\/CVPR.2016.308"},{"key":"1735_CR50","doi-asserted-by":"crossref","unstructured":"Tao, X., Gao, H., Liao, R., Wang, J., & Jia, J. (2017). Detail-revealing deep video super-resolution. In The IEEE international conference on computer vision (ICCV), Oct.","DOI":"10.1109\/ICCV.2017.479"},{"key":"1735_CR51","unstructured":"Unterthiner, T., Steenkiste, S., Kurach, K., Marinier, R., Michalski, M., & Gelly, S. (2018). Towards accurate generative models of video: A new metric & challenges. arXiv preprint arXiv:1812.01717."},{"issue":"7","key":"1735_CR52","doi-asserted-by":"publisher","first-page":"1032","DOI":"10.1109\/TCSVT.2010.2051366","volume":"20","author":"G Varghese","year":"2010","unstructured":"Varghese, G., & Wang, Z. (2010). Video denoising based on a spatiotemporal gaussian scale mixture model. IEEE Transactions on Circuits and Systems for Video Technology, 20(7), 1032\u20131040.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"1735_CR53","doi-asserted-by":"crossref","unstructured":"Wang, T., Chen, M., & Chao, H. (2017). A novel deep learning-based method of improving coding efficiency from the decoder-end for hevc. In 2017 data compression conference (DCC) (pp. 410\u2013419). IEEE.","DOI":"10.1109\/DCC.2017.42"},{"key":"1735_CR54","doi-asserted-by":"crossref","unstructured":"Wang, X., Chan, K.C.K., Yu, K., Dong, C., & Change\u00a0Loy, C. (2019). Edvr: Video restoration with enhanced deformable convolutional networks. In The IEEE conference on computer vision and pattern recognition (CVPR) workshops, June.","DOI":"10.1109\/CVPRW.2019.00247"},{"key":"1735_CR55","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., & Loy, C.C. (2018). Esrgan: Enhanced super-resolution generative adversarial networks. In The European conference on computer vision workshops (ECCVW), September.","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"1735_CR56","doi-asserted-by":"crossref","unstructured":"Werlberger, M., Pock, T., Unger, M., & Bischof, H. (2011). Optical flow guided tv-l 1 video interpolation and restoration. In International workshop on energy minimization methods in computer vision and pattern recognition (pp. 273\u2013286). Springer.","DOI":"10.1007\/978-3-642-23094-3_20"},{"key":"1735_CR57","unstructured":"Xiong, W., Liu, D., Shen, X., Fang, C., & Luo, J. (2020). Unsupervised real-world low-light image enhancement with decoupled networks. arXiv preprint arXiv:2005.02818."},{"issue":"8","key":"1735_CR58","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, W. T. (2019). Video enhancement with task-oriented flow. International Journal of Computer Vision, 127(8), 1106\u20131125.","journal-title":"International Journal of Computer Vision"},{"key":"1735_CR59","doi-asserted-by":"crossref","unstructured":"Yang, R., Xu, M., Wang, Z., & Li, T. (2018). Multi-frame quality enhancement for compressed video. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6664\u20136673).","DOI":"10.1109\/CVPR.2018.00697"},{"issue":"7","key":"1735_CR60","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1109\/TCSVT.2013.2242631","volume":"23","author":"Z Yu","year":"2013","unstructured":"Yu, Z., Li, H., Wang, Z., Hu, Z., & Chen, C. W. (2013). Multi-level video frame interpolation: Exploiting the interaction among different levels. IEEE Transactions on Circuits and Systems for Video Technology, 23(7), 1235\u20131248.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"1735_CR61","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O.(2018). The unreasonable effectiveness of deep features as a perceptual metric. In CVPR: The unreasonable effectiveness of deep features as a perceptual metric.","DOI":"10.1109\/CVPR.2018.00068"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-022-01735-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-022-01735-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-022-01735-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T05:10:41Z","timestamp":1677820241000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-022-01735-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,8]]},"references-count":61,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["1735"],"URL":"https:\/\/doi.org\/10.1007\/s11263-022-01735-0","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,8]]},"assertion":[{"value":"21 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}