{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T10:28:00Z","timestamp":1760956080384,"version":"3.40.3"},"publisher-location":"Cham","reference-count":66,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030585167"},{"type":"electronic","value":"9783030585174"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58517-4_44","type":"book-chapter","created":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T19:03:11Z","timestamp":1602270191000},"page":"749-766","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks"],"prefix":"10.1007","author":[{"given":"Majed","family":"El Helou","sequence":"first","affiliation":[]},{"given":"Ruofan","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Sabine","family":"S\u00fcsstrunk","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,10]]},"reference":[{"key":"44_CR1","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R.: NTIRE 2017 challenge on single image super-resolution: dataset and study. In: CVPR Workshops (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"issue":"11","key":"44_CR2","doi-asserted-by":"publisher","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","volume":"54","author":"M Aharon","year":"2006","unstructured":"Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311\u20134322 (2006)","journal-title":"IEEE Trans. Signal Process."},{"issue":"1","key":"44_CR3","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/T-C.1974.223784","volume":"100","author":"N Ahmed","year":"1974","unstructured":"Ahmed, N., Natarajan, T., Rao, K.R.: Discrete cosine transform. IEEE Trans. Comput. 100(1), 90\u201393 (1974)","journal-title":"IEEE Trans. Comput."},{"key":"44_CR4","doi-asserted-by":"crossref","unstructured":"Anwar, S., Barnes, N.: Real image denoising with feature attention. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00325"},{"issue":"9","key":"44_CR5","doi-asserted-by":"publisher","first-page":"2112","DOI":"10.1109\/TPAMI.2018.2855177","volume":"41","author":"S Anwar","year":"2018","unstructured":"Anwar, S., Huynh, C.P., Porikli, F.: Image deblurring with a class-specific prior. IEEE Trans. Pattern Anal. Mach. Intell. 41(9), 2112\u20132130 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"44_CR6","unstructured":"Batson, J., Royer, L.: Noise2Self: blind denoising by self-supervision. In: ICML (2019)"},{"key":"44_CR7","doi-asserted-by":"publisher","first-page":"A132","DOI":"10.1051\/0004-6361\/201220752","volume":"556","author":"S Beckouche","year":"2013","unstructured":"Beckouche, S., Starck, J.L., Fadili, J.: Astronomical image denoising using dictionary learning. Astron. Astrophys. 556, A132 (2013)","journal-title":"Astron. Astrophys."},{"issue":"11","key":"44_CR8","doi-asserted-by":"publisher","first-page":"1814","DOI":"10.1109\/TIP.2005.857247","volume":"14","author":"A Benazza-Benyahia","year":"2005","unstructured":"Benazza-Benyahia, A., Pesquet, J.C.: Building robust wavelet estimators for multicomponent images using Stein\u2019s principle. IEEE Trans. Image Process. 14(11), 1814\u20131830 (2005)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"44_CR9","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1364\/AO.26.000157","volume":"26","author":"GJ Burton","year":"1987","unstructured":"Burton, G.J., Moorhead, I.R.: Color and spatial structure in natural scenes. Appl. Opt. 26(1), 157\u2013170 (1987)","journal-title":"Appl. Opt."},{"key":"44_CR10","doi-asserted-by":"crossref","unstructured":"Cai, J., Zeng, H., Yong, H., Cao, Z., Zhang, L.: Toward real-world single image super-resolution: a new benchmark and a new model. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00318"},{"issue":"5","key":"44_CR11","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1016\/j.patrec.2008.11.008","volume":"30","author":"TM Chan","year":"2009","unstructured":"Chan, T.M., Zhang, J., Pu, J., Huang, H.: Neighbor embedding based super-resolution algorithm through edge detection and feature selection. Pattern Recogn. Lett. 30(5), 494\u2013502 (2009)","journal-title":"Pattern Recogn. Lett."},{"key":"44_CR12","doi-asserted-by":"crossref","unstructured":"Chatterjee, P., Joshi, N., Kang, S.B., Matsushita, Y.: Noise suppression in low-light images through joint denoising and demosaicing. In: CVPR (2011)","DOI":"10.1109\/CVPR.2011.5995371"},{"issue":"6","key":"44_CR13","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.1109\/TPAMI.2016.2596743","volume":"39","author":"Y Chen","year":"2016","unstructured":"Chen, Y., Pock, T.: Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1256\u20131272 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"8","key":"44_CR14","doi-asserted-by":"publisher","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","volume":"16","author":"K Dabov","year":"2007","unstructured":"Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080\u20132095 (2007)","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"44_CR15","doi-asserted-by":"publisher","first-page":"1620","DOI":"10.1109\/TIP.2012.2235847","volume":"22","author":"W Dong","year":"2012","unstructured":"Dong, W., Zhang, L., Shi, G., Li, X.: Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process. 22(4), 1620\u20131630 (2012)","journal-title":"IEEE Trans. Image Process."},{"key":"44_CR16","doi-asserted-by":"crossref","unstructured":"Efrat, N., Glasner, D., Apartsin, A., Nadler, B., Levin, A.: Accurate blur models vs. image priors in single image super-resolution. In: ICCV (2013)","DOI":"10.1109\/ICCV.2013.352"},{"key":"44_CR17","doi-asserted-by":"crossref","unstructured":"El Helou, M., D\u00fcmbgen, F., S\u00fcsstrunk, S.: AAM: an assessment metric of axial chromatic aberration. In: ICIP (2018)","DOI":"10.1109\/ICIP.2018.8451377"},{"key":"44_CR18","doi-asserted-by":"publisher","first-page":"4885","DOI":"10.1109\/TIP.2020.2976814","volume":"29","author":"M El Helou","year":"2020","unstructured":"El Helou, M., S\u00fcsstrunk, S.: Blind universal Bayesian image denoising with Gaussian noise level learning. IEEE Trans. Image Process. 29, 4885\u20134897 (2020)","journal-title":"IEEE Trans. Image Process."},{"issue":"12","key":"44_CR19","doi-asserted-by":"publisher","first-page":"2379","DOI":"10.1364\/JOSAA.4.002379","volume":"4","author":"DJ Field","year":"1987","unstructured":"Field, D.J.: Relations between the statistics of natural images and the response properties of cortical cells. JOSA 4(12), 2379\u20132394 (1987)","journal-title":"JOSA"},{"issue":"10","key":"44_CR20","doi-asserted-by":"publisher","first-page":"1737","DOI":"10.1109\/TIP.2008.2001399","volume":"17","author":"A Foi","year":"2008","unstructured":"Foi, A., Trimeche, M., Katkovnik, V., Egiazarian, K.: Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data. IEEE Trans. Image Process. 17(10), 1737\u20131754 (2008)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"44_CR21","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/38.988747","volume":"22","author":"WT Freeman","year":"2002","unstructured":"Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. Comput. Graph. Appl. 22(2), 56\u201365 (2002)","journal-title":"Comput. Graph. Appl."},{"key":"44_CR22","doi-asserted-by":"crossref","unstructured":"Gu, J., Lu, H., Zuo, W.Z., Dong, C.: Blind super-resolution with iterative kernel correction. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00170"},{"key":"44_CR23","doi-asserted-by":"crossref","unstructured":"Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.366"},{"issue":"5","key":"44_CR24","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1109\/TIP.2003.811513","volume":"12","author":"BK Gunturk","year":"2003","unstructured":"Gunturk, B.K., Batur, A.U., Altunbasak, Y., Hayes, M.H., Mersereau, R.M.: Eigenface-domain super-resolution for face recognition. IEEE Trans. Image Process. 12(5), 597\u2013606 (2003)","journal-title":"IEEE Trans. Image Process."},{"key":"44_CR25","doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: ECCV (2016)","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"44_CR26","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.neunet.2018.09.009","volume":"110","author":"SH Khan","year":"2019","unstructured":"Khan, S.H., Hayat, M., Porikli, F.: Regularization of deep neural networks with spectral dropout. Neural Netw. 110, 82\u201390 (2019)","journal-title":"Neural Netw."},{"key":"44_CR27","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"44_CR28","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"44_CR29","doi-asserted-by":"crossref","unstructured":"Krull, A., Buchholz, T.O., Jug, F.: Noise2Void-learning denoising from single noisy images. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00223"},{"key":"44_CR30","doi-asserted-by":"crossref","unstructured":"Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"44_CR31","doi-asserted-by":"crossref","unstructured":"Lefkimmiatis, S.: Universal denoising networks: a novel CNN architecture for image denoising. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00338"},{"key":"44_CR32","unstructured":"Lehtinen, J., et al.: Noise2Noise: learning image restoration without clean data. In: ICML (2018)"},{"issue":"12","key":"44_CR33","doi-asserted-by":"publisher","first-page":"3450","DOI":"10.1109\/TBME.2012.2217493","volume":"59","author":"S Li","year":"2012","unstructured":"Li, S., Yin, H., Fang, L.: Group-sparse representation with dictionary learning for medical image denoising and fusion. IEEE Trans. Biomed. Eng. 59(12), 3450\u20133459 (2012)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"3","key":"44_CR34","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1109\/TIP.2010.2073477","volume":"20","author":"F Luisier","year":"2011","unstructured":"Luisier, F., Blu, T., Unser, M.: Image denoising in mixed Poisson-Gaussian noise. IEEE Trans. Image Process. 20(3), 696\u2013708 (2011)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"44_CR35","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1109\/TIP.2012.2202675","volume":"22","author":"M Makitalo","year":"2012","unstructured":"Makitalo, M., Foi, A.: Optimal inversion of the generalized Anscombe transformation for Poisson-Gaussian noise. IEEE Trans. Image Process. 22(1), 91\u2013103 (2012)","journal-title":"IEEE Trans. Image Process."},{"key":"44_CR36","unstructured":"Martin, D., Fowlkes, C., Tal, D., Malik, J., et al.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: ICCV (2001)"},{"key":"44_CR37","doi-asserted-by":"crossref","unstructured":"Park, D.S., et al.: SpecAugment: a simple data augmentation method for automatic speech recognition. arXiv preprint arXiv:1904.08779 (2019)","DOI":"10.21437\/Interspeech.2019-2680"},{"issue":"1","key":"44_CR38","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1002\/1522-2594(200101)45:1<29::AID-MRM1005>3.0.CO;2-Z","volume":"45","author":"S Peled","year":"2001","unstructured":"Peled, S., Yeshurun, Y.: Superresolution in MRI: application to human white matter fiber tract visualization by diffusion tensor imaging. Magn. Reson. Med. Official J. Int. Soc. Magn. Reson. Med. 45(1), 29\u201335 (2001)","journal-title":"Magn. Reson. Med. Official J. Int. Soc. Magn. Reson. Med."},{"key":"44_CR39","unstructured":"Pl\u00f6tz, T., Roth, S.: Neural nearest neighbors networks. In: NeurIPS (2018)"},{"key":"44_CR40","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"2","key":"44_CR41","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s11263-008-0197-6","volume":"82","author":"S Roth","year":"2009","unstructured":"Roth, S., Black, M.J.: Fields of experts. Int. J. Comput. Vision 82(2), 205 (2009)","journal-title":"Int. J. Comput. Vision"},{"key":"44_CR42","doi-asserted-by":"crossref","unstructured":"Schmidt, U., Roth, S.: Shrinkage fields for effective image restoration. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.349"},{"key":"44_CR43","doi-asserted-by":"crossref","unstructured":"Schulter, S., Leistner, C., Bischof, H.: Fast and accurate image upscaling with super-resolution forests. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7299003"},{"key":"44_CR44","doi-asserted-by":"crossref","unstructured":"Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"44_CR45","doi-asserted-by":"crossref","unstructured":"Shi, W., et al.: Cardiac image super-resolution with global correspondence using multi-atlas patchMatch. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 9\u201316 (2013)","DOI":"10.1007\/978-3-642-40760-4_2"},{"key":"44_CR46","doi-asserted-by":"crossref","unstructured":"Shocher, A., Cohen, N., Irani, M.: Zero-shot super-resolution using deep internal learning. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00329"},{"issue":"1","key":"44_CR47","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1137\/S0036144598336745","volume":"41","author":"G Strang","year":"1999","unstructured":"Strang, G.: The discrete cosine transform. SIAM 41(1), 135\u2013147 (1999)","journal-title":"SIAM"},{"key":"44_CR48","unstructured":"Sun, J., Xu, Z., Shum, H.Y.: Image super-resolution using gradient profile prior. In: CVPR (2008)"},{"key":"44_CR49","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., Liu, X., Xu, C.: MemNet: a persistent memory network for image restoration. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.486"},{"issue":"3","key":"44_CR50","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1080\/01431160500207088","volume":"27","author":"MW Thornton","year":"2006","unstructured":"Thornton, M.W., Atkinson, P.M., Holland, D.: Sub-pixel mapping of rural land cover objects from fine spatial resolution satellite sensor imagery using super-resolution pixel-swapping. Int. J. Remote Sens. 27(3), 473\u2013491 (2006)","journal-title":"Int. J. Remote Sens."},{"issue":"2","key":"44_CR51","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1111\/j.1475-1313.1992.tb00296.x","volume":"12","author":"D Tolhurst","year":"1992","unstructured":"Tolhurst, D., Tadmor, Y., Chao, T.: Amplitude spectra of natural images. Ophthalmic Physiol. Opt. 12(2), 229\u2013232 (1992)","journal-title":"Ophthalmic Physiol. Opt."},{"issue":"3","key":"44_CR52","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1088\/0954-898X_14_3_302","volume":"14","author":"A Torralba","year":"2003","unstructured":"Torralba, A., Oliva, A.: Statistics of natural image categories. Netw. Comput. Neural Syst. 14(3), 391\u2013412 (2003)","journal-title":"Netw. Comput. Neural Syst."},{"key":"44_CR53","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior. In: CVPR (2018)"},{"key":"44_CR54","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: ESRGAN: enhanced super-resolution generative adversarial networks. In: ECCV Workshops (2018)","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"44_CR55","doi-asserted-by":"crossref","unstructured":"Yang, C.Y., Ma, C., Yang, M.H.: Single-image super-resolution: a benchmark. In: ECCV (2014)","DOI":"10.1007\/978-3-319-10593-2_25"},{"issue":"7","key":"44_CR56","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142\u20133155 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"44_CR57","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Gu, S., Zhang, L.: Learning deep CNN denoiser prior for image restoration. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.300"},{"issue":"9","key":"44_CR58","doi-asserted-by":"publisher","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","volume":"27","author":"K Zhang","year":"2018","unstructured":"Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27(9), 4608\u20134622 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"44_CR59","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Zhang, L.: Learning a single convolutional super-resolution network for multiple degradations. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00344"},{"key":"44_CR60","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Zhang, L.: Deep plug-and-play super-resolution for arbitrary blur kernels. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00177"},{"key":"44_CR61","doi-asserted-by":"crossref","unstructured":"Zhang, X., Chen, Q., Ng, R., Koltu, V.: Zoom to learn, learn to zoom. In: ICCV (2019)","DOI":"10.1109\/CVPR.2019.00388"},{"key":"44_CR62","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: A poisson-Gaussian denoising dataset with real fluorescence microscopy images. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01198"},{"key":"44_CR63","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"44_CR64","doi-asserted-by":"publisher","first-page":"135-1","DOI":"10.2352\/ISSN.2470-1173.2019.13.COIMG-135","volume":"13","author":"R Zhou","year":"2019","unstructured":"Zhou, R., Lahoud, F., El Helou, M., S\u00fcsstrunk, S.: A comparative study on wavelets and residuals in deep super resolution. Electron. Imaging 13, 135-1\u2013135-6 (2019)","journal-title":"Electron. Imaging"},{"key":"44_CR65","doi-asserted-by":"crossref","unstructured":"Zhou, R., S\u00fcsstrunk, S.: Kernel modeling super-resolution on real low-resolution images. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00252"},{"key":"44_CR66","doi-asserted-by":"crossref","unstructured":"Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: ICCV (2011)","DOI":"10.1109\/ICCV.2011.6126278"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58517-4_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:18:35Z","timestamp":1728433115000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58517-4_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585167","9783030585174"],"references-count":66,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58517-4_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"10 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1360","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}