{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:38:06Z","timestamp":1777657086735,"version":"3.51.4"},"publisher-location":"Cham","reference-count":55,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031732089","type":"print"},{"value":"9783031732096","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73209-6_5","type":"book-chapter","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T15:02:57Z","timestamp":1730386977000},"page":"74-91","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["A Comparative Study of\u00a0Image Restoration Networks for\u00a0General Backbone Network Design"],"prefix":"10.1007","author":[{"given":"Xiangyu","family":"Chen","sequence":"first","affiliation":[]},{"given":"Zheyuan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yuandong","family":"Pu","sequence":"additional","affiliation":[]},{"given":"Yihao","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jiantao","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Qiao","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Dong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"5_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/978-3-030-58607-2_7","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Abuolaim","year":"2020","unstructured":"Abuolaim, A., Brown, M.S.: Defocus deblurring using dual-pixel data. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12355, pp. 111\u2013126. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58607-2_7"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., Morel, M.L.A.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: British Machine Vision Conference (BMVC) (2012)","DOI":"10.5244\/C.26.135"},{"issue":"11","key":"5_CR3","doi-asserted-by":"publisher","first-page":"5187","DOI":"10.1109\/TIP.2016.2598681","volume":"25","author":"B Cai","year":"2016","unstructured":"Cai, B., Xu, X., Jia, K., Qing, C., Tao, D.: Dehazenet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 25(11), 5187\u20135198 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Chen, H., et al.: Pre-trained image processing transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12299\u201312310 (2021)","DOI":"10.1109\/CVPR46437.2021.01212"},{"key":"5_CR5","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/978-3-031-20071-7_2","volume-title":"ECCV","author":"L Chen","year":"2022","unstructured":"Chen, L., Chu, X., Zhang, X., Sun, J.: Simple baselines for image restoration. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV. LNCS, vol. 13667, pp. 17\u201333. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20071-7_2"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Chen, L., Lu, X., Zhang, J., Chu, X., Chen, C.: Hinet: half instance normalization network for image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 182\u2013192 (2021)","DOI":"10.1109\/CVPRW53098.2021.00027"},{"key":"5_CR7","unstructured":"Chen, X., et al.: Hat: hybrid attention transformer for image restoration. arXiv preprint arXiv:2309.05239 (2023)"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Chen, X., Wang, X., Zhou, J., Qiao, Y., Dong, C.: Activating more pixels in image super-resolution transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 22367\u201322377 (2023)","DOI":"10.1109\/CVPR52729.2023.02142"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhang, Y., Gu, J., Kong, L., Yang, X., Yu, F.: Dual aggregation transformer for image super-resolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12312\u201312321 (2023)","DOI":"10.1109\/ICCV51070.2023.01131"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Cho, S.J., Ji, S.W., Hong, J.P., Jung, S.W., Ko, S.J.: Rethinking coarse-to-fine approach in single image deblurring. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4641\u20134650 (2021)","DOI":"10.1109\/ICCV48922.2021.00460"},{"key":"5_CR11","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/978-3-031-20071-7_4","volume-title":"ECCV 2022","author":"X Chu","year":"2022","unstructured":"Chu, X., Chen, L., Chen, C., Lu, X.: Improving image restoration by revisiting global information aggregation. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13667, pp. 53\u201371. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20071-7_4"},{"key":"5_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1007\/978-3-030-58577-8_12","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J Dong","year":"2020","unstructured":"Dong, J., Pan, J.: Physics-based feature dehazing networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12375, pp. 188\u2013204. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58577-8_12"},{"key":"5_CR13","unstructured":"Franzen, R.: Kodak lossless true color image suite. (1999). http:\/\/r0k.us\/graphics\/kodak4(2)"},{"key":"5_CR14","doi-asserted-by":"crossref","unstructured":"Fu, X., Huang, J., Zeng, D., Huang, Y., Ding, X., Paisley, J.: Removing rain from single images via a deep detail network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3855\u20133863 (2017)","DOI":"10.1109\/CVPR.2017.186"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Gu, J., Dong, C.: Interpreting super-resolution networks with local attribution maps. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9199\u20139208 (2021)","DOI":"10.1109\/CVPR46437.2021.00908"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197\u20135206 (2015)","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Jiang, K., et al.: Multi-scale progressive fusion network for single image deraining. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8346\u20138355 (2020)","DOI":"10.1109\/CVPR42600.2020.00837"},{"issue":"1","key":"5_CR19","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1109\/TIP.2018.2867951","volume":"28","author":"B Li","year":"2018","unstructured":"Li, B., et al.: Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process. 28(1), 492\u2013505 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"5_CR20","unstructured":"Li, W., Lu, X., Qian, S., Lu, J., Zhang, X., Jia, J.: On efficient transformer-based image pre-training for low-level vision. arXiv preprint arXiv:2112.10175 (2021)"},{"key":"5_CR21","unstructured":"Li, Y., et\u00a0al.: Ntire 2023 challenge on image denoising: methods and results. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1904\u20131920 (2023)"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Li, Y., Tan, R.T., Guo, X., Lu, J., Brown, M.S.: Rain streak removal using layer priors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2736\u20132744 (2016)","DOI":"10.1109\/CVPR.2016.299"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van\u00a0Gool, L., Timofte, R.: Swinir: image restoration using swin transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1833\u20131844 (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Mu\u00a0Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136\u2013144 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"5_CR25","unstructured":"Lin, Z., et al.: Revisiting rcan: improved training for image super-resolution (2022)"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Liu, J., Yang, W., Yang, S., Guo, Z.: Erase or fill? deep joint recurrent rain removal and reconstruction in videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3233\u20133242 (2018)","DOI":"10.1109\/CVPR.2018.00341"},{"key":"5_CR27","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1007\/978-3-031-19797-0_26","volume-title":"ECCV 2022","author":"L Liu","year":"2022","unstructured":"Liu, L., et al.: TAPE: task-agnostic prior embedding for image restoration. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13678, pp. 447\u2013464. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19797-0_26"},{"key":"5_CR28","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"5_CR29","doi-asserted-by":"crossref","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 IEEE International Conference on Computer Vision, vol.\u00a02, pp. 416\u2013423. IEEE (2001)","DOI":"10.1109\/ICCV.2001.937655"},{"issue":"20","key":"5_CR30","doi-asserted-by":"publisher","first-page":"21811","DOI":"10.1007\/s11042-016-4020-z","volume":"76","author":"Y Matsui","year":"2017","unstructured":"Matsui, Y., et al.: Sketch-based manga retrieval using manga109 dataset. Multimedia Tools Appl. 76(20), 21811\u201321838 (2017)","journal-title":"Multimedia Tools Appl."},{"key":"5_CR31","doi-asserted-by":"crossref","unstructured":"Nah, S., Hyun\u00a0Kim, T., Mu\u00a0Lee, K.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3883\u20133891 (2017)","DOI":"10.1109\/CVPR.2017.35"},{"key":"5_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/978-3-030-58610-2_12","volume-title":"Computer Vision \u2013 ECCV 2020","author":"B Niu","year":"2020","unstructured":"Niu, B., et al.: Single image super-resolution via a holistic attention network. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 191\u2013207. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58610-2_12"},{"key":"5_CR33","doi-asserted-by":"crossref","unstructured":"Purohit, K., Suin, M., Rajagopalan, A., Boddeti, V.N.: Spatially-adaptive image restoration using distortion-guided networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2309\u20132319 (2021)","DOI":"10.1109\/ICCV48922.2021.00231"},{"key":"5_CR34","doi-asserted-by":"crossref","unstructured":"Ren, D., Zuo, W., Hu, Q., Zhu, P., Meng, D.: Progressive image deraining networks: a better and simpler baseline. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3937\u20133946 (2019)","DOI":"10.1109\/CVPR.2019.00406"},{"key":"5_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/978-3-030-58595-2_12","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J Rim","year":"2020","unstructured":"Rim, J., Lee, H., Won, J., Cho, S.: Real-world blur dataset for learning and benchmarking deblurring algorithms. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 184\u2013201. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58595-2_12"},{"key":"5_CR36","doi-asserted-by":"crossref","unstructured":"Shen, Z., et al.: Human-aware motion deblurring. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5572\u20135581 (2019)","DOI":"10.1109\/ICCV.2019.00567"},{"key":"5_CR37","doi-asserted-by":"publisher","first-page":"1927","DOI":"10.1109\/TIP.2023.3256763","volume":"32","author":"Y Song","year":"2023","unstructured":"Song, Y., He, Z., Qian, H., Du, X.: Vision transformers for single image dehazing. IEEE Trans. Image Process. 32, 1927\u20131941 (2023)","journal-title":"IEEE Trans. Image Process."},{"key":"5_CR38","doi-asserted-by":"crossref","unstructured":"Tu, Z., et al.: Maxim: multi-axis MLP for image processing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5769\u20135780 (2022)","DOI":"10.1109\/CVPR52688.2022.00568"},{"key":"5_CR39","unstructured":"Wang, X., Xie, L., Yu, K., Chan, K.C., Loy, C.C., Dong, C.: BasicSR: open source image and video restoration toolbox. https:\/\/github.com\/XPixelGroup\/BasicSR (2022)"},{"key":"5_CR40","doi-asserted-by":"crossref","unstructured":"Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., Li, H.: Uformer: a general u-shaped transformer for image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17683\u201317693 (2022)","DOI":"10.1109\/CVPR52688.2022.01716"},{"key":"5_CR41","doi-asserted-by":"crossref","unstructured":"Wu, H., et al.: Contrastive learning for compact single image dehazing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10551\u201310560 (2021)","DOI":"10.1109\/CVPR46437.2021.01041"},{"key":"5_CR42","doi-asserted-by":"crossref","unstructured":"Yang, W., Tan, R.T., Feng, J., Liu, J., Guo, Z., Yan, S.: Deep joint rain detection and removal from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1357\u20131366 (2017)","DOI":"10.1109\/CVPR.2017.183"},{"key":"5_CR43","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H.: Restormer: efficient transformer for high-resolution image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5728\u20135739 (2022)","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"5_CR44","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., et al.: Multi-stage progressive image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14821\u201314831 (2021)","DOI":"10.1109\/CVPR46437.2021.01458"},{"key":"5_CR45","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/978-3-642-27413-8_47","volume-title":"Curves and Surfaces","author":"R Zeyde","year":"2012","unstructured":"Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D., et al. (eds.) Curves and Surfaces 2010. LNCS, vol. 6920, pp. 711\u2013730. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-27413-8_47"},{"key":"5_CR46","doi-asserted-by":"crossref","unstructured":"Zhang, H., Patel, V.M.: Density-aware single image de-raining using a multi-stream dense network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 695\u2013704 (2018)","DOI":"10.1109\/CVPR.2018.00079"},{"issue":"10","key":"5_CR47","doi-asserted-by":"publisher","first-page":"6360","DOI":"10.1109\/TPAMI.2021.3088914","volume":"44","author":"K Zhang","year":"2021","unstructured":"Zhang, K., Li, Y., Zuo, W., Zhang, L., Van Gool, L., Timofte, R.: Plug-and-play image restoration with deep denoiser prior. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6360\u20136376 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"7","key":"5_CR48","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":"5_CR49","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Gu, S., Zhang, L.: Learning deep CNN denoiser prior for image restoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3929\u20133938 (2017)","DOI":"10.1109\/CVPR.2017.300"},{"issue":"9","key":"5_CR50","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."},{"issue":"2","key":"5_CR51","doi-asserted-by":"publisher","first-page":"023016","DOI":"10.1117\/1.3600632","volume":"20","author":"L Zhang","year":"2011","unstructured":"Zhang, L., Wu, X., Buades, A., Li, X.: Color demosaicking by local directional interpolation and nonlocal adaptive thresholding. J. Electron. Imaging 20(2), 023016\u2013023016 (2011)","journal-title":"J. Electron. Imaging"},{"key":"5_CR52","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":"5_CR53","unstructured":"Zhang, Y., Li, K., Li, K., Zhong, B., Fu, Y.: Residual non-local attention networks for image restoration. arXiv preprint arXiv:1903.10082 (2019)"},{"key":"5_CR54","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 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2472\u20132481 (2018)","DOI":"10.1109\/CVPR.2018.00262"},{"issue":"7","key":"5_CR55","doi-asserted-by":"publisher","first-page":"2480","DOI":"10.1109\/TPAMI.2020.2968521","volume":"43","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 43(7), 2480\u20132495 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73209-6_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T12:24:14Z","timestamp":1744115054000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73209-6_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,1]]},"ISBN":["9783031732089","9783031732096"],"references-count":55,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73209-6_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,1]]},"assertion":[{"value":"1 November 2024","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":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}