{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T05:22:20Z","timestamp":1776316940421,"version":"3.50.1"},"publisher-location":"Cham","reference-count":80,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031197963","type":"print"},{"value":"9783031197970","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19797-0_26","type":"book-chapter","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T20:28:41Z","timestamp":1667420921000},"page":"447-464","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["TAPE: Task-Agnostic Prior Embedding for\u00a0Image Restoration"],"prefix":"10.1007","author":[{"given":"Lin","family":"Liu","sequence":"first","affiliation":[]},{"given":"Lingxi","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Xiaopeng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Shanxin","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Xiangyu","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Wengang","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Houqiang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Qi","family":"Tian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"26_CR1","doi-asserted-by":"crossref","unstructured":"Abdelhamed, A., Lin, S., Brown, M.S.: A high-quality denoising dataset for smartphone cameras. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00182"},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R.: Ntire 2017 challenge on single image super-resolution: Dataset and study. In: CVPRW (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"26_CR3","first-page":"12","volume":"18","author":"SD Babacan","year":"2008","unstructured":"Babacan, S.D., Molina, R., Katsaggelos, A.K.: Variational Bayesian blind deconvolution using a total variation prior. TIP 18, 12\u201326 (2008)","journal-title":"TIP"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Baek, K., Choi, Y., Uh, Y., Yoo, J., Shim, H.: Rethinking the truly unsupervised image-to-image translation. In: International Conference on Computer Vision (ICCV, 2021) (2021)","DOI":"10.1109\/ICCV48922.2021.01389"},{"key":"26_CR5","unstructured":"Bau, D., et al.: Semantic photo manipulation with a generative image prior. arXiv preprint arXiv:2005.07727 (2020)"},{"key":"26_CR6","unstructured":"Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. In: ICLR (2018)"},{"key":"26_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213\u2013229. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Chan, K.C., Wang, X., Xu, X., Gu, J., Loy, C.C.: Glean: generative latent bank for large-factor image super-resolution. arXiv preprint arXiv:2012.00739 (2020)","DOI":"10.1109\/CVPR46437.2021.01402"},{"key":"26_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/978-3-030-58577-8_11","volume-title":"Computer Vision \u2013 ECCV 2020","author":"M Chang","year":"2020","unstructured":"Chang, M., Li, Q., Feng, H., Xu, Z.: Spatial-adaptive network for single image denoising. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12375, pp. 171\u2013187. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58577-8_11"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Chen, H., et al.: Pre-trained image processing transformer. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01212"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Chen, L., Fang, F., Wang, T., Zhang, G.: Blind image deblurring with local maximum gradient prior. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00184"},{"key":"26_CR12","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.E.: A simple framework for contrastive learning of visual representations. In: ICML (2020)"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Chen, X., Wang, X., Zhou, J., Dong, C.: Activating more pixels in image super-resolution transformer. arXiv preprint arXiv:2205.04437 (2022)","DOI":"10.1109\/CVPR52729.2023.02142"},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Chen, Y.L., Hsu, C.T.: A generalized low-rank appearance model for spatio-temporally correlated rain streaks. In: ICCV (2013)","DOI":"10.1109\/ICCV.2013.247"},{"key":"26_CR15","unstructured":"Dai, T., et al.: Wavelet-based network for high dynamic range imaging. arXiv preprint 2108.01434 (2021)"},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Dai, Z., Cai, B., Lin, Y., Chen, J.: Up-detr: unsupervised pre-training for object detection with transformers. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00165"},{"key":"26_CR17","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. TPAMI 38, 295\u2013307 (2015)","journal-title":"TPAMI"},{"key":"26_CR18","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"26_CR19","unstructured":"El Helou, M., S\u00fcsstrunk, S.: BIGPrior: towards decoupling learned prior hallucination and data fidelity in image restoration. arXiv preprint arXiv:2011.01406 (2020)"},{"key":"26_CR20","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: CVPR (2017)","DOI":"10.1109\/CVPR.2017.186"},{"key":"26_CR21","first-page":"2692","volume":"29","author":"A Golts","year":"2020","unstructured":"Golts, A., Freedman, D., Elad, M.: Unsupervised single image dehazing using dark channel prior loss. TIP 29, 2692\u20132701 (2020)","journal-title":"TIP"},{"key":"26_CR22","doi-asserted-by":"crossref","unstructured":"Gu, J., Shen, Y., Zhou, B.: Image processing using multi-code GAN prior. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00308"},{"key":"26_CR23","doi-asserted-by":"crossref","unstructured":"Guo, S., Liang, Z., Zhang, L.: Joint denoising and demosaicking with green channel prior for real-world burst images. arXiv preprint arXiv:2101.09870 (2021)","DOI":"10.1109\/TIP.2021.3100312"},{"key":"26_CR24","doi-asserted-by":"crossref","unstructured":"Guo, S., Yan, Z., Zhang, K., Zuo, W., Zhang, L.: Toward convolutional blind denoising of real photographs. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00181"},{"key":"26_CR25","doi-asserted-by":"crossref","unstructured":"He, B., Wang, C., Shi, B., Duan, L.Y.: Mop moire patterns using mopnet. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00251"},{"key":"26_CR26","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"26_CR27","first-page":"2341","volume":"33","author":"K He","year":"2010","unstructured":"He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. TPAMI 33, 2341\u20132353 (2010)","journal-title":"TPAMI"},{"key":"26_CR28","doi-asserted-by":"crossref","unstructured":"Isobe, T., et al.: Video super-resolution with temporal group attention. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00803"},{"key":"26_CR29","doi-asserted-by":"crossref","unstructured":"Jiang, K., Wang, Z., Yi, P., Chen, C., Lin, C.W.: PCNet: progressive coupled network for real-time image deraining. In: TIP (2021)","DOI":"10.1109\/ICIP42928.2021.9506318"},{"key":"26_CR30","doi-asserted-by":"crossref","unstructured":"Kupyn, O., Martyniuk, T., Wu, J., Wang, Z.: Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00897"},{"key":"26_CR31","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1109\/LSP.2020.2965824","volume":"27","author":"H Lee","year":"2020","unstructured":"Lee, H., Sohn, K., Min, D.: Unsupervised low-light image enhancement using bright channel prior. IEEE Signal Process. Lett. 27, 251\u2013255 (2020)","journal-title":"IEEE Signal Process. Lett."},{"key":"26_CR32","doi-asserted-by":"crossref","unstructured":"Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Understanding and evaluating blind deconvolution algorithms. In: CVPR (2009)","DOI":"10.1109\/CVPR.2009.5206815"},{"key":"26_CR33","doi-asserted-by":"crossref","unstructured":"Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., Peng, X.: All-in-one image restoration for unknown corruption. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01693"},{"key":"26_CR34","doi-asserted-by":"publisher","first-page":"1025","DOI":"10.1007\/s11263-018-01146-0","volume":"127","author":"L Li","year":"2019","unstructured":"Li, L., Pan, J., Lai, W.S., Gao, C., Sang, N., Yang, M.H.: Blind image deblurring via deep discriminative priors. IJCV 127, 1025\u20131043 (2019)","journal-title":"IJCV"},{"key":"26_CR35","unstructured":"Li, W., et al.: Sj-hd$$^2r$$: Selective joint high dynamic range and denoising imaging for dynamic scenes. arXiv preprint 2206.09611 (2022)"},{"key":"26_CR36","unstructured":"Li, W., Lu, X., Lu, J., Zhang, X., Jia, J.: On efficient transformer and image pre-training for low-level vision. arXiv preprint arXiv:2112.10175"},{"key":"26_CR37","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1007\/978-3-030-01234-2_16","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Li","year":"2018","unstructured":"Li, X., Wu, J., Lin, Z., Liu, H., Zha, H.: Recurrent squeeze-and-excitation context aggregation net for single image deraining. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 262\u2013277. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_16"},{"key":"26_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/978-3-030-58526-6_19","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X Li","year":"2020","unstructured":"Li, X., et al.: Learning disentangled feature representation for hybrid-distorted image restoration. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 313\u2013329. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58526-6_19"},{"key":"26_CR39","doi-asserted-by":"crossref","unstructured":"Li, Y., Tan, R.T., Guo, X., Lu, J., Brown, M.S.: Rain streak removal using layer priors. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.299"},{"key":"26_CR40","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: Swinir: image restoration using swin transformer. In: ICCVW 2021","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"26_CR41","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1007\/978-3-030-58601-0_6","volume-title":"Computer Vision \u2013 ECCV 2020","author":"L Liu","year":"2020","unstructured":"Liu, L., et al.: Wavelet-based dual-branch network for image Demoir\u00e9ing. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12358, pp. 86\u2013102. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58601-0_6"},{"key":"26_CR42","doi-asserted-by":"crossref","unstructured":"Liu, L., Yuan, S., Liu, J., Guo, X., Yan, Y., Tian, Q.: Siamtrans: zero-shot multi-frame image restoration with pre-trained siamese transformers. In: AAAI (2022)","DOI":"10.1609\/aaai.v36i2.20067"},{"key":"26_CR43","first-page":"3064","volume":"27","author":"YF Liu","year":"2018","unstructured":"Liu, Y.F., Jaw, D.W., Huang, S.C., Hwang, J.N.: Desnownet: context-aware deep network for snow removal. TIP 27, 3064\u20133073 (2018)","journal-title":"TIP"},{"key":"26_CR44","doi-asserted-by":"crossref","unstructured":"Nah, S., et al.: Ntire 2019 challenge on video deblurring and super-resolution: Dataset and study. In: CVPRW (2019)","DOI":"10.1109\/CVPRW.2019.00251"},{"key":"26_CR45","doi-asserted-by":"crossref","unstructured":"Pan, J., Bai, H., Tang, J.: Cascaded deep video deblurring using temporal sharpness prior. In: CVPR, 2020","DOI":"10.1109\/CVPR42600.2020.00311"},{"key":"26_CR46","doi-asserted-by":"crossref","unstructured":"Pan, J., Sun, D., Pfister, H., Yang, M.H.: Blind image deblurring using dark channel prior. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.180"},{"key":"26_CR47","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1007\/978-3-030-58536-5_16","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X Pan","year":"2020","unstructured":"Pan, X., Zhan, X., Dai, B., Lin, D., Loy, C.C., Luo, P.: Exploiting deep generative prior for versatile image restoration and manipulation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 262\u2013277. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58536-5_16"},{"key":"26_CR48","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/978-3-030-58545-7_19","volume-title":"Computer Vision \u2013 ECCV 2020","author":"T Park","year":"2020","unstructured":"Park, T., Efros, A.A., Zhang, R., Zhu, J.-Y.: Contrastive learning for unpaired image-to-image translation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 319\u2013345. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58545-7_19"},{"key":"26_CR49","doi-asserted-by":"crossref","unstructured":"Qian, R., Tan, R.T., Yang, W., Su, J., Liu, J.: Attentive generative adversarial network for raindrop removal from a single image. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00263"},{"key":"26_CR50","first-page":"6852","volume":"29","author":"D Ren","year":"2020","unstructured":"Ren, D., Shang, W., Zhu, P., Hu, Q., Meng, D., Zuo, W.: Single image deraining using bilateral recurrent network. TIP 29, 6852\u20136863 (2020)","journal-title":"TIP"},{"key":"26_CR51","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: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00406"},{"key":"26_CR52","doi-asserted-by":"crossref","unstructured":"Richardson, E., et al.: Encoding in style: a stylegan encoder for image-to-image translation. arXiv preprint arXiv:2008.00951 (2020)","DOI":"10.1109\/CVPR46437.2021.00232"},{"key":"26_CR53","unstructured":"Roth, S., Black, M.J.: Fields of experts: a framework for learning image priors. In: CVPR (2005)"},{"key":"26_CR54","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","volume":"60","author":"LI Rudin","year":"1992","unstructured":"Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259\u2013268 (1992)","journal-title":"Physica D"},{"key":"26_CR55","first-page":"4160","volume":"27","author":"Y Sun","year":"2018","unstructured":"Sun, Y., Yu, Y., Wang, W.: Moir\u00e9 photo restoration using multiresolution convolutional neural networks. TIP 27, 4160\u20134172 (2018)","journal-title":"TIP"},{"key":"26_CR56","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior. In: CVPR (2018)"},{"key":"26_CR57","unstructured":"Vaswani, A., et al.: Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)"},{"key":"26_CR58","doi-asserted-by":"crossref","unstructured":"Wang, J., Li, X., Yang, J.: Stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00192"},{"key":"26_CR59","doi-asserted-by":"crossref","unstructured":"Wang, T., Yang, X., Xu, K., Chen, S., Zhang, Q., Lau, R.W.: Spatial attentive single-image deraining with a high quality real rain dataset. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01255"},{"key":"26_CR60","doi-asserted-by":"crossref","unstructured":"Wang, X., Li, Y., Zhang, H., Shan, Y.: Towards real-world blind face restoration with generative facial prior. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00905"},{"key":"26_CR61","unstructured":"Wang, Z., Cun, X., Bao, J., Liu, J.: Uformer: a general u-shaped transformer for image restoration. arXiv preprint arXiv:2106.03106"},{"key":"26_CR62","doi-asserted-by":"crossref","unstructured":"Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00583"},{"key":"26_CR63","doi-asserted-by":"crossref","unstructured":"Yang, S., Lei, Y., Xiong, S., Wang, W.: High resolution demoire network. In: ICIP (2020)","DOI":"10.1109\/ICIP40778.2020.9191255"},{"key":"26_CR64","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: CVPR (2017)","DOI":"10.1109\/CVPR.2017.183"},{"key":"26_CR65","doi-asserted-by":"crossref","unstructured":"Yi, Q., Li, J., Dai, Q., Fang, F., Zhang, G., Zeng, T.: Structure-preserving deraining with residue channel prior guidance. ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00420"},{"key":"26_CR66","doi-asserted-by":"crossref","unstructured":"Yu, K., Dong, C., Lin, L., Loy, C.C.: Crafting a toolchain for image restoration by deep reinforcement learning. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00259"},{"key":"26_CR67","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: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"26_CR68","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., et al.: Multi-stage progressive image restoration. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01458"},{"key":"26_CR69","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1007\/978-3-030-58517-4_31","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Zeng","year":"2020","unstructured":"Zeng, Y., Fu, J., Chao, H.: Learning joint spatial-temporal transformations for video inpainting. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 528\u2013543. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58517-4_31"},{"key":"26_CR70","first-page":"3142","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. TIP 26, 3142\u20133155 (2017)","journal-title":"TIP"},{"key":"26_CR71","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"},{"key":"26_CR72","first-page":"4608","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. TIP 27, 4608\u20134622 (2018)","journal-title":"TIP"},{"key":"26_CR73","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. TPAMI 43, 2480\u20132495 (2020)","journal-title":"TPAMI"},{"key":"26_CR74","doi-asserted-by":"crossref","unstructured":"Zheng, B., et al.: Domainplus: cross transform domain learning towards efficient high dynamic range imaging. In: ACM MM (2022)","DOI":"10.1145\/3503161.3547823"},{"key":"26_CR75","doi-asserted-by":"crossref","unstructured":"Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: CVPR, 2020","DOI":"10.1109\/CVPR42600.2020.00369"},{"key":"26_CR76","doi-asserted-by":"publisher","first-page":"7705","DOI":"10.1109\/TPAMI.2021.3115139","volume":"44","author":"B Zheng","year":"2021","unstructured":"Zheng, B., et al.: Learning frequency domain priors for image demoireing. TPAMI 44, 7705\u20137717 (2021)","journal-title":"TPAMI"},{"key":"26_CR77","doi-asserted-by":"crossref","unstructured":"Zhu, L., Fu, C.W., Lischinski, D., Heng, P.A.: Joint bi-layer optimization for single-image rain streak removal. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.276"},{"key":"26_CR78","first-page":"3522","volume":"24","author":"Q Zhu","year":"2015","unstructured":"Zhu, Q., Mai, J., Shao, L.: A fast single image haze removal algorithm using color attenuation prior. TIP 24, 3522\u20133533 (2015)","journal-title":"TIP"},{"key":"26_CR79","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.1109\/34.632983","volume":"19","author":"SC Zhu","year":"1997","unstructured":"Zhu, S.C., Mumford, D.: Prior learning and Gibbs reaction-diffusion. TPAMI 19, 1236\u20131250 (1997)","journal-title":"TPAMI"},{"key":"26_CR80","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: deformable transformers for end-to-end object detection. In: ICLR (2021)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19797-0_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T23:50:39Z","timestamp":1701301839000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19797-0_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031197963","9783031197970"],"references-count":80,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19797-0_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"3 November 2022","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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)"}}]}}