{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:17:59Z","timestamp":1782836279641,"version":"3.54.5"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030110208","type":"print"},{"value":"9783030110215","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-11021-5_21","type":"book-chapter","created":{"date-parts":[[2019,1,24]],"date-time":"2019-01-24T04:26:13Z","timestamp":1548303973000},"page":"334-355","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":355,"title":["The 2018 PIRM Challenge on Perceptual Image Super-Resolution"],"prefix":"10.1007","author":[{"given":"Yochai","family":"Blau","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roey","family":"Mechrez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Radu","family":"Timofte","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomer","family":"Michaeli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihi","family":"Zelnik-Manor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,1,23]]},"reference":[{"key":"21_CR1","doi-asserted-by":"crossref","unstructured":"Blau, Y., Michaeli, T.: The perception-distortion tradeoff. In: Proceedings of the CVPR (2018)","DOI":"10.1109\/CVPR.2018.00652"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Cheon, M., Kim, J.H., Choi, J.H., Lee, J.S.: Generative adversarial network-based image super-resolution using perceptual content losses. In: Proceedings of the ECCV Workshops (2018)","DOI":"10.1007\/978-3-030-11021-5_4"},{"key":"21_CR3","unstructured":"Choi, J.H., Kim, J.H., Cheon, M., Lee, J.S.: Deep learning-based image super-resolution considering quantitative and perceptual quality. arXiv preprint arXiv:1809.04789 (2018)"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Dahl, R., Norouzi, M., Shlens, J.: Pixel recursive super resolution. In: Proceedings of the ICCV (2017)","DOI":"10.1109\/ICCV.2017.581"},{"issue":"4","key":"21_CR5","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1109\/LSP.2018.2805809","volume":"25","author":"X Deng","year":"2018","unstructured":"Deng, X.: Enhancing image quality via style transfer for single image super-resolution. IEEE Sign. Process. Lett. 25(4), 571\u2013575 (2018)","journal-title":"IEEE Sign. Process. Lett."},{"key":"21_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/978-3-319-10593-2_13","volume-title":"Computer Vision \u2013 ECCV 2014","author":"C Dong","year":"2014","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184\u2013199. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10593-2_13"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Gatys, L., Ecker, A.S., Bethge, M.: Texture synthesis using convolutional neural networks. In: Proceedings of the NIPS (2015)","DOI":"10.1109\/CVPR.2016.265"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Gondal, M.W., Sch\u00f6lkopf, B., Hirsch, M.: The unreasonable effectiveness of texture transfer for single image super-resolution. In: Proceedings of the ECCV Workshops (2018)","DOI":"10.1007\/978-3-030-11021-5_6"},{"key":"21_CR9","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Proceedings of the NIPS (2014)"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Han, W., Chang, S., Liu, D., Yu, M., Witbrock, M., Huang, T.S.: Image super-resolution via dual-state recurrent networks. In: Proceedings of the CVPR (2018)","DOI":"10.1109\/CVPR.2018.00178"},{"key":"21_CR11","doi-asserted-by":"crossref","unstructured":"Haris, M., Shakhnarovich, G., Ukita, N.: Deep backprojection networks for super-resolution. In: Proceedings of the CVPR (2018)","DOI":"10.1109\/CVPR.2018.00179"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the CVPR (2015)","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"21_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-319-46475-6_43","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Johnson","year":"2016","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694\u2013711. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43"},{"key":"21_CR14","unstructured":"Jolicoeur-Martineau, A.: The relativistic discriminator: a key element missing from standard GAN. arXiv preprint arXiv:1807.00734 (2018)"},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the CVPR (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Kim, J.H., Lee, J.S.: Deep residual network with enhanced upscaling module for super-resolution. In: Proceedings of the CVPR Workshops (2018)","DOI":"10.1109\/CVPRW.2018.00124"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Deep laplacian pyramid networks for fast and accurate superresolution. In: Proceedings of the CVPR (2017)","DOI":"10.1109\/CVPR.2017.618"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the CVPR (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the CVPR workshops (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the ICCV (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Luo, X., Chen, R., Xie, Y., Qu, Y., Cui-hua, L.: Bi-GANs-ST for perceptual image super-resolution. In: Proceedings of the ECCV Workshops (2018)","DOI":"10.1007\/978-3-030-11021-5_2"},{"key":"21_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cviu.2016.12.009","volume":"158","author":"C Ma","year":"2017","unstructured":"Ma, C., Yang, C.Y., Yang, X., Yang, M.H.: Learning a no-reference quality metric for single-image super-resolution. Comput. Vis. Image Underst. 158, 1\u201316 (2017)","journal-title":"Comput. Vis. Image Underst."},{"key":"21_CR23","unstructured":"Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the ICCV (2001)"},{"key":"21_CR24","unstructured":"Mechrez, R., Talmi, I., Shama, F., Zelnik-Manor, L.: Learning to maintain natural image statistics. arXiv preprint arXiv:1803.04626 (2018)"},{"key":"21_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1007\/978-3-030-01264-9_47","volume-title":"Computer Vision \u2013 ECCV 2018","author":"R Mechrez","year":"2018","unstructured":"Mechrez, R., Talmi, I., Zelnik-Manor, L.: The contextual loss for image transformation with non-aligned data. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018. LNCS, vol. 11218, pp. 800\u2013815. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01264-9_47"},{"issue":"12","key":"21_CR26","doi-asserted-by":"publisher","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","volume":"21","author":"A Mittal","year":"2012","unstructured":"Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. (TIP) 21(12), 4695\u20134708 (2012)","journal-title":"IEEE Trans. Image Process. (TIP)"},{"issue":"3","key":"21_CR27","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1109\/LSP.2012.2227726","volume":"20","author":"A Mittal","year":"2013","unstructured":"Mittal, A., Soundararajan, R., Bovik, A.C.: Making a \u201ccompletely blind\u201d image quality analyzer. IEEE Sign. Process. Lett. 20(3), 209\u2013212 (2013)","journal-title":"IEEE Sign. Process. Lett."},{"key":"21_CR28","doi-asserted-by":"crossref","unstructured":"Navarrete Michelini, P., Zhu, D., Hanwen, L.: Multi-scale recursive and perception-distortion controllable image super-resolution. In: Proceedings of the ECCV Workshops (2018)","DOI":"10.1007\/978-3-030-11021-5_1"},{"key":"21_CR29","doi-asserted-by":"crossref","unstructured":"Purohit, K., Mandal, S., Rajagopalan, A.N.: Scale-recurrent multi-residual dense network for image super resolution. In: Proceedings of the ECCV Workshops (2018)","DOI":"10.1007\/978-3-030-11021-5_9"},{"issue":"8","key":"21_CR30","doi-asserted-by":"publisher","first-page":"3339","DOI":"10.1109\/TIP.2012.2191563","volume":"21","author":"MA Saad","year":"2012","unstructured":"Saad, M.A., Bovik, A.C., Charrier, C.: Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Trans. Image Process. (TIP) 21(8), 3339\u20133352 (2012)","journal-title":"IEEE Trans. Image Process. (TIP)"},{"key":"21_CR31","doi-asserted-by":"crossref","unstructured":"Sajjadi, M.S., Sch\u00f6lkopf, B., Hirsch, M.: Enhancenet: single image super-resolution through automated texture synthesis. In: Proceedings of the ICCV (2017)","DOI":"10.1109\/ICCV.2017.481"},{"issue":"2","key":"21_CR32","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1109\/TIP.2005.859378","volume":"15","author":"HR Sheikh","year":"2006","unstructured":"Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. (TIP) 15(2), 430\u2013444 (2006)","journal-title":"IEEE Trans. Image Process. (TIP)"},{"issue":"12","key":"21_CR33","doi-asserted-by":"publisher","first-page":"2117","DOI":"10.1109\/TIP.2005.859389","volume":"14","author":"HR Sheikh","year":"2005","unstructured":"Sheikh, H.R., Bovik, A.C., De Veciana, G.: An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans. Image Process. 14(12), 2117\u20132128 (2005)","journal-title":"IEEE Trans. Image Process."},{"key":"21_CR34","doi-asserted-by":"crossref","unstructured":"Shocher, A., Cohen, N., Irani, M.: \u201czero-shot\u201d super-resolution using deep internal learning. In: Proceedings of the CVPR (2018)","DOI":"10.1109\/CVPR.2018.00329"},{"key":"21_CR35","unstructured":"Sun, L., Hays, J.: Super-resolution using constrained deep texture synthesis. arXiv preprint arXiv:1701.07604 (2017)"},{"key":"21_CR36","doi-asserted-by":"crossref","unstructured":"Timofte, R., Agustsson, E., Van Gool, L., Yang, M.H., Zhang, L., et al.: NTIRE 2017 challenge on single image super-resolution: methods and results. In: Proceedings of the CVPR workshops (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"21_CR37","doi-asserted-by":"crossref","unstructured":"Timofte, R., De Smet, V., Van Gool, L.: A+: adjusted anchored neighborhood regression for fast super-resolution. In: Proceedings of the ACCV (2014)","DOI":"10.1109\/ICCV.2013.241"},{"key":"21_CR38","unstructured":"Timofte, R., et al.: NTIRE 2018 challenge on single image super-resolution: methods and results. In: Proceedings of the CVPR workshops (2018)"},{"key":"21_CR39","doi-asserted-by":"crossref","unstructured":"Tong, T., Li, G., Liu, X., Gao, Q.: Image super-resolution using dense skip connections. In: Proceedings of the ICCV (2017)","DOI":"10.1109\/ICCV.2017.514"},{"key":"21_CR40","doi-asserted-by":"crossref","unstructured":"Vasu, S., Nimisha, T.M., Rajagopalan, A.N.: Analyzing perception-distortion tradeoff using enhanced perceptual super-resolution network. In: Proceedings of the ECCV Workshops (2018)","DOI":"10.1007\/978-3-030-11021-5_8"},{"key":"21_CR41","doi-asserted-by":"crossref","unstructured":"Vu, T., Luu, T., Yoo, C.D.: Perception-enhanced image super-resolution via relativistic generative adversarial networks. In: Proceedings of the ECCV Workshops (2018)","DOI":"10.1007\/978-3-030-11021-5_7"},{"key":"21_CR42","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Dong, C., Loy, C.C.: Recovering realistic texture in image super-resolution by deep spatial feature transform. In: Proceedings of the CVPR (2018)","DOI":"10.1109\/CVPR.2018.00070"},{"key":"21_CR43","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: ESRGAN: enhanced super-resolution generative adversarial networks. In: Proceedings of the ECCV Workshops (2018)","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"21_CR44","doi-asserted-by":"crossref","unstructured":"Wang, Y., Perazzi, F., McWilliams, B., Sorkine-Hornung, A., Sorkine-Hornung, O., Schroers, C.: A fully progressive approach to single-image super-resolution. In: Proceedings of the CVPR (2018)","DOI":"10.1109\/CVPRW.2018.00131"},{"issue":"4","key":"21_CR45","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. (TIP) 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process. (TIP)"},{"key":"21_CR46","unstructured":"Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: Conference on Signals, Systems & Computers, vol. 2, pp. 1398\u20131402 (2003)"},{"key":"21_CR47","unstructured":"Yang, W., Zhang, X., Tian, Y., Wang, W., Xue, J.H.: Deep learning for single image super-resolution: a brief review. arXiv preprint arXiv:1808.03344 (2018)"},{"key":"21_CR48","unstructured":"Ye, P., Kumar, J., Kang, L., Doermann, D.: Unsupervised feature learning framework for no-reference image quality assessment. In: Proceedings of the CVPR (2012)"},{"issue":"8","key":"21_CR49","doi-asserted-by":"publisher","first-page":"2378","DOI":"10.1109\/TIP.2011.2109730","volume":"20","author":"L Zhang","year":"2011","unstructured":"Zhang, L., et al.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. (TIP) 20(8), 2378\u20132386 (2011)","journal-title":"IEEE Trans. Image Process. (TIP)"},{"key":"21_CR50","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the CVPR (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"21_CR51","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1007\/978-3-030-01234-2_18","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 294\u2013310. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_18"},{"key":"21_CR52","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: Proceedings of the CVPR (2018)","DOI":"10.1109\/CVPR.2018.00262"},{"issue":"1","key":"21_CR53","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","volume":"3","author":"H Zhao","year":"2017","unstructured":"Zhao, H., Gallo, O., Frosio, I., Kautz, J.: Loss functions for image restoration with neural networks. IEEE Trans. Comput. Imaging 3(1), 47\u201357 (2017)","journal-title":"IEEE Trans. Comput. Imaging"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2018 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-11021-5_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,22]],"date-time":"2023-01-22T01:15:28Z","timestamp":1674350128000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-11021-5_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030110208","9783030110215"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-11021-5_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 January 2019","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":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2018.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}