{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:02:12Z","timestamp":1780765332509,"version":"3.54.1"},"publisher-location":"Cham","reference-count":94,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200700","type":"print"},{"value":"9783031200717","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\/978-3-031-20071-7_6","type":"book-chapter","created":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T05:15:09Z","timestamp":1668230109000},"page":"91-110","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["D2HNet: Joint Denoising and\u00a0Deblurring with\u00a0Hierarchical Network for\u00a0Robust Night Image Restoration"],"prefix":"10.1007","author":[{"given":"Yuzhi","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongzhe","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiong","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dingdong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuehui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lai-Man","family":"Po","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,13]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Abdelhamed, A., Brubaker, M.A., Brown, M.S.: Noise flow: noise modeling with conditional normalizing flows. In: Proceedings ICCV, pp. 3165\u20133173 (2019)","DOI":"10.1109\/ICCV.2019.00326"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Anwar, S., Barnes, N.: Real image denoising with feature attention. In: Proceedings ICCV, pp. 3155\u20133164 (2019)","DOI":"10.1109\/ICCV.2019.00325"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Brooks, T., Mildenhall, B., Xue, T., Chen, J., Sharlet, D., Barron, J.T.: Unprocessing images for learned raw denoising. In: Proceedings CVPR, pp. 11036\u201311045 (2019)","DOI":"10.1109\/CVPR.2019.01129"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: Proceedings CVPR, vol. 2, pp. 60\u201365 (2005)","DOI":"10.1109\/CVPR.2005.38"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Byun, J., Cha, S., Moon, T.: FBI-denoiser: fast blind image denoiser for poisson-gaussian noise. In: Proceedings CVPR, pp. 5768\u20135777 (2021)","DOI":"10.1109\/CVPR46437.2021.00571"},{"key":"6_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/978-3-319-46487-9_14","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Chakrabarti","year":"2016","unstructured":"Chakrabarti, A.: A neural approach to blind motion deblurring. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 221\u2013235. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_14"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Chan, K.C., Wang, X., Yu, K., Dong, C., Loy, C.C.: BasicVSR: the search for essential components in video super-resolution and beyond. In: Proceedings CVPR, pp. 4947\u20134956 (2021)","DOI":"10.1109\/CVPR46437.2021.00491"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Chan, K.C., Zhou, S., Xu, X., Loy, C.C.: BasicVSR++: improving video super-resolution with enhanced propagation and alignment. In: Proceedings CVPR, pp. 5972\u20135981 (2022)","DOI":"10.1109\/CVPR52688.2022.00588"},{"key":"6_CR9","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1109\/TMM.2021.3058586","volume":"24","author":"M Chang","year":"2021","unstructured":"Chang, M., Feng, H., Xu, Z., Li, Q.: Low-light image restoration with short-and long-exposure raw pairs. IEEE Trans. Multimedia 24, 702\u2013714 (2021)","journal-title":"IEEE Trans. Multimedia"},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Chen, C., Chen, Q., Xu, J., Koltun, V.: Learning to see in the dark. In: Proceedings CVPR, pp. 3291\u20133300 (2018)","DOI":"10.1109\/CVPR.2018.00347"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Chen, J., Chen, J., Chao, H., Yang, M.: Image blind denoising with generative adversarial network based noise modeling. In: Proceedings CVPR, pp. 3155\u20133164 (2018)","DOI":"10.1109\/CVPR.2018.00333"},{"key":"6_CR12","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 CVPRW, pp. 182\u2013192 (2021)","DOI":"10.1109\/CVPRW53098.2021.00027"},{"issue":"6","key":"6_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."},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Cheng, S., Wang, Y., Huang, H., Liu, D., Fan, H., Liu, S.: NbNet: noise basis learning for image denoising with subspace projection. In: Proceedings CVPR, pp. 4896\u20134906 (2021)","DOI":"10.1109\/CVPR46437.2021.00486"},{"key":"6_CR15","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 ICCV, pp. 4641\u20134650 (2021)","DOI":"10.1109\/ICCV48922.2021.00460"},{"issue":"3","key":"6_CR16","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1109\/TCE.2008.4637576","volume":"54","author":"BD Choi","year":"2008","unstructured":"Choi, B.D., Jung, S.W., Ko, S.J.: Motion-blur-free camera system splitting exposure time. IEEE Trans. Consum. Electron. 54(3), 981\u2013986 (2008)","journal-title":"IEEE Trans. Consum. Electron."},{"issue":"8","key":"6_CR17","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."},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Dai, J., et al.: Deformable convolutional networks. In: Proceedings ICCV, pp. 764\u2013773 (2017)","DOI":"10.1109\/ICCV.2017.89"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Deng, J., Wang, L., Pu, S., Zhuo, C.: Spatio-temporal deformable convolution for compressed video quality enhancement. In: Proceedings, AAAI. vol. 34, pp. 10696\u201310703 (2020)","DOI":"10.1609\/aaai.v34i07.6697"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Dudhane, A., Zamir, S.W., Khan, S., Khan, F.S., Yang, M.H.: Burst image restoration and enhancement. In: Proceedings CVPR, pp. 5759\u20135768 (2022)","DOI":"10.1109\/CVPR52688.2022.00567"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Gao, H., Tao, X., Shen, X., Jia, J.: Dynamic scene deblurring with parameter selective sharing and nested skip connections. In: Proceedings CVPR, pp. 3848\u20133856 (2019)","DOI":"10.1109\/CVPR.2019.00397"},{"key":"6_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1007\/978-3-030-01267-0_33","volume-title":"Computer Vision \u2013 ECCV 2018","author":"C Godard","year":"2018","unstructured":"Godard, C., Matzen, K., Uyttendaele, M.: Deep burst denoising. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 560\u2013577. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01267-0_33"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Gong, D., et al.: From motion blur to motion flow: a deep learning solution for removing heterogeneous motion blur. In: Proceedings CVPR, pp. 2319\u20132328 (2017)","DOI":"10.1109\/CVPR.2017.405"},{"key":"6_CR24","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1109\/TIP.2020.3036745","volume":"30","author":"C Gu","year":"2020","unstructured":"Gu, C., Lu, X., He, Y., Zhang, C.: Blur removal via blurred-noisy image pair. IEEE Trans. Image Process. 30, 345\u2013359 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Gu, S., Li, Y., Gool, L.V., Timofte, R.: Self-guided network for fast image denoising. In: Proceedings ICCV, pp. 2511\u20132520 (2019)","DOI":"10.1109\/ICCV.2019.00260"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Guo, S., Yan, Z., Zhang, K., Zuo, W., Zhang, L.: Toward convolutional blind denoising of real photographs. In: Proceedings CVPR, pp. 1712\u20131722 (2019)","DOI":"10.1109\/CVPR.2019.00181"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"Guo, S., Yang, X., Ma, J., Ren, G., Zhang, L.: A differentiable two-stage alignment scheme for burst image reconstruction with large shift. In: Proceedings CVPR, pp. 17472\u201317481 (2022)","DOI":"10.1109\/CVPR52688.2022.01695"},{"key":"6_CR28","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"6_CR29","doi-asserted-by":"crossref","unstructured":"Hu, X., et al.: Pseudo 3D auto-correlation network for real image denoising. In: Proceedings CVPR, pp. 16175\u201316184 (2021)","DOI":"10.1109\/CVPR46437.2021.01591"},{"key":"6_CR30","doi-asserted-by":"crossref","unstructured":"Ji, S.W., et al.: XYDeblur: divide and conquer for single image deblurring. In: Proceedings CVPR, pp. 17421\u201317430 (2022)","DOI":"10.1109\/CVPR52688.2022.01690"},{"key":"6_CR31","doi-asserted-by":"crossref","unstructured":"Jiang, H., Sun, D., Jampani, V., Yang, M.H., Learned-Miller, E., Kautz, J.: Super slomo: high quality estimation of multiple intermediate frames for video interpolation. In: Proceedings CVPR, pp. 9000\u20139008 (2018)","DOI":"10.1109\/CVPR.2018.00938"},{"key":"6_CR32","doi-asserted-by":"publisher","first-page":"9372","DOI":"10.1109\/TIP.2021.3125394","volume":"30","author":"AS Karadeniz","year":"2021","unstructured":"Karadeniz, A.S., Erdem, E., Erdem, A.: Burst photography for learning to enhance extremely dark images. IEEE Trans. Image Process. 30, 9372\u20139385 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"6_CR33","doi-asserted-by":"crossref","unstructured":"Kim, Y., Soh, J.W., Park, G.Y., Cho, N.I.: Transfer learning from synthetic to real-noise denoising with adaptive instance normalization. In: Proceedings CVPR, pp. 3482\u20133492 (2020)","DOI":"10.1109\/CVPR42600.2020.00354"},{"key":"6_CR34","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings ICLR (2014)"},{"key":"6_CR35","unstructured":"Krishnan, D., Fergus, R.: Fast image deconvolution using hyper-Laplacian priors. In: Proceedings NeurIPS, pp. 1033\u20131041 (2009)"},{"key":"6_CR36","doi-asserted-by":"crossref","unstructured":"Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., Matas, J.: Deblurgan: blind motion deblurring using conditional adversarial networks. In: Proceedings CVPR, pp. 8183\u20138192 (2018)","DOI":"10.1109\/CVPR.2018.00854"},{"key":"6_CR37","doi-asserted-by":"crossref","unstructured":"Kupyn, O., Martyniuk, T., Wu, J., Wang, Z.: Deblurgan-v2: deblurring (orders-of-magnitude) faster and better. In: Proceedings ICCV, pp. 8878\u20138887 (2019)","DOI":"10.1109\/ICCV.2019.00897"},{"key":"6_CR38","doi-asserted-by":"crossref","unstructured":"Lamba, M., Mitra, K.: Restoring extremely dark images in real time. In: Proceedings CVPR, pp. 3487\u20133497 (2021)","DOI":"10.1109\/CVPR46437.2021.00349"},{"key":"6_CR39","doi-asserted-by":"crossref","unstructured":"Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Efficient marginal likelihood optimization in blind deconvolution. In: Proceedings CVPR, pp. 2657\u20132664 (2011)","DOI":"10.1109\/CVPR.2011.5995308"},{"issue":"5","key":"6_CR40","doi-asserted-by":"publisher","first-page":"2614","DOI":"10.1109\/TIP.2018.2887342","volume":"28","author":"H Li","year":"2018","unstructured":"Li, H., Wu, X.J.: DenseFuse: a fusion approach to infrared and visible images. IEEE Trans. Image Process. 28(5), 2614\u20132623 (2018)","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"6_CR41","doi-asserted-by":"publisher","first-page":"2828","DOI":"10.1109\/TIP.2018.2810539","volume":"27","author":"M Li","year":"2018","unstructured":"Li, M., Liu, J., Yang, W., Sun, X., Guo, Z.: Structure-revealing low-light image enhancement via robust retinex model. IEEE Trans. Image Process. 27(6), 2828\u20132841 (2018)","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"6_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3355089.3356508","volume":"38","author":"O Liba","year":"2019","unstructured":"Liba, O., et al.: Handheld mobile photography in very low light. ACM Trans. Graph. 38(6), 1\u201316 (2019)","journal-title":"ACM Trans. Graph."},{"key":"6_CR43","doi-asserted-by":"crossref","unstructured":"Liu, P., Zhang, H., Zhang, K., Lin, L., Zuo, W.: Multi-level wavelet-CNN for image restoration. In: Proceedings CVPRW, pp. 773\u2013782 (2018)","DOI":"10.1109\/CVPRW.2018.00121"},{"key":"6_CR44","doi-asserted-by":"crossref","unstructured":"Liu, W., Yan, Q., Zhao, Y.: Densely self-guided wavelet network for image denoising. In: Proceedings CVPRW, pp. 432\u2013433 (2020)","DOI":"10.1109\/CVPRW50498.2020.00224"},{"key":"6_CR45","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Invertible denoising network: a light solution for real noise removal. In: Proceedings CVPR, pp. 13365\u201313374 (2021)","DOI":"10.1109\/CVPR46437.2021.01316"},{"issue":"6","key":"6_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2661229.2661277","volume":"33","author":"Z Liu","year":"2014","unstructured":"Liu, Z., Yuan, L., Tang, X., Uyttendaele, M., Sun, J.: Fast burst images denoising. ACM Trans. Graph. 33(6), 1\u20139 (2014)","journal-title":"ACM Trans. Graph."},{"key":"6_CR47","doi-asserted-by":"crossref","unstructured":"Luo, Z., et al.: EBSR: feature enhanced burst super-resolution with deformable alignment. In: Proceedings CVPRW, pp. 471\u2013478 (2021)","DOI":"10.1109\/CVPRW53098.2021.00058"},{"key":"6_CR48","unstructured":"Mao, X., Shen, C., Yang, Y.B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: Proceedings NeurIPS, pp. 2802\u20132810 (2016)"},{"key":"6_CR49","doi-asserted-by":"crossref","unstructured":"Mildenhall, B., Barron, J.T., Chen, J., Sharlet, D., Ng, R., Carroll, R.: Burst denoising with kernel prediction networks. In: Proceedings CVPR, pp. 2502\u20132510 (2018)","DOI":"10.1109\/CVPR.2018.00265"},{"key":"6_CR50","unstructured":"Mustaniemi, J., Kannala, J., Matas, J., S\u00e4rkk\u00e4, S., Heikkil\u00e4, J.: Lsd$$_2$$ - joint denoising and deblurring of short and long exposure images with convolutional neural networks. In: Proceedings BMVC (2020)"},{"key":"6_CR51","doi-asserted-by":"crossref","unstructured":"Nah, S., Hyun Kim, T., Mu Lee, K.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proceedings CVPR, pp. 3883\u20133891 (2017)","DOI":"10.1109\/CVPR.2017.35"},{"key":"6_CR52","doi-asserted-by":"crossref","unstructured":"Nimisha, T.M., Kumar Singh, A., Rajagopalan, A.N.: Blur-invariant deep learning for blind-deblurring. In: Proceedings ICCV, pp. 4752\u20134760 (2017)","DOI":"10.1109\/ICCV.2017.509"},{"key":"6_CR53","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/978-3-030-58539-6_20","volume-title":"Computer Vision \u2013 ECCV 2020","author":"D Park","year":"2020","unstructured":"Park, D., Kang, D.U., Kim, J., Chun, S.Y.: Multi-temporal recurrent neural networks for progressive non-uniform single image deblurring with incremental temporal training. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12351, pp. 327\u2013343. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58539-6_20"},{"key":"6_CR54","doi-asserted-by":"crossref","unstructured":"Purohit, K., Rajagopalan, A.: Region-adaptive dense network for efficient motion deblurring. In: Proceedings AAAI, vol. 34, pp. 11882\u201311889 (2020)","DOI":"10.1609\/aaai.v34i07.6862"},{"key":"6_CR55","doi-asserted-by":"crossref","unstructured":"Ren, C., He, X., Wang, C., Zhao, Z.: Adaptive consistency prior based deep network for image denoising. In: Proceedings CVPR, pp. 8596\u20138606 (2021)","DOI":"10.1109\/CVPR46437.2021.00849"},{"issue":"1","key":"6_CR56","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1364\/JOSA.62.000055","volume":"62","author":"WH Richardson","year":"1972","unstructured":"Richardson, W.H.: Bayesian-based iterative method of image restoration. JoSA 62(1), 55\u201359 (1972)","journal-title":"JoSA"},{"key":"6_CR57","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":"6_CR58","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Proceedings MICCAI, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"1\u20134","key":"6_CR59","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(1\u20134), 259\u2013268 (1992)","journal-title":"Physica D"},{"key":"6_CR60","doi-asserted-by":"crossref","unstructured":"Simoncelli, E.P., Adelson, E.H.: Noise removal via Bayesian wavelet coring. In: Proceedings ICIP, vol. 1, pp. 379\u2013382 (1996)","DOI":"10.1109\/ICIP.1996.559512"},{"key":"6_CR61","doi-asserted-by":"crossref","unstructured":"Son, C.H., Choo, H., Park, H.M.: Image-pair-based deblurring with spatially varying norms and noisy image updating. J. Vis. Comm. Image Rep. 24(8), 1303\u20131315 (2013)","DOI":"10.1016\/j.jvcir.2013.09.001"},{"issue":"4","key":"6_CR62","doi-asserted-by":"publisher","first-page":"1791","DOI":"10.1109\/TCE.2011.6131155","volume":"57","author":"CH Son","year":"2011","unstructured":"Son, C.H., Park, H.M.: A pair of noisy\/blurry patches-based PSF estimation and channel-dependent deblurring. IEEE Trans. Consum. Electron. 57(4), 1791\u20131799 (2011)","journal-title":"IEEE Trans. Consum. Electron."},{"key":"6_CR63","doi-asserted-by":"crossref","unstructured":"Suin, M., Purohit, K., Rajagopalan, A.: Spatially-attentive patch-hierarchical network for adaptive motion deblurring. In: Proceedings, pp. 3606\u20133615 (2020)","DOI":"10.1109\/CVPR42600.2020.00366"},{"key":"6_CR64","doi-asserted-by":"crossref","unstructured":"Sun, J., Cao, W., Xu, Z., Ponce, J.: Learning a convolutional neural network for non-uniform motion blur removal. In: Proceedings, CVPR. pp. 769\u2013777 (2015)","DOI":"10.1109\/CVPR.2015.7298677"},{"key":"6_CR65","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., Liu, X., Xu, C.: MemNet: a persistent memory network for image restoration. In: Proceedings ICCV, pp. 4539\u20134547 (2017)","DOI":"10.1109\/ICCV.2017.486"},{"key":"6_CR66","doi-asserted-by":"crossref","unstructured":"Tao, X., Gao, H., Shen, X., Wang, J., Jia, J.: Scale-recurrent network for deep image deblurring. In: Proceedings CVPR, pp. 8174\u20138182 (2018)","DOI":"10.1109\/CVPR.2018.00853"},{"key":"6_CR67","doi-asserted-by":"crossref","unstructured":"Tian, Y., Zhang, Y., Fu, Y., Xu, C.: TDAN: temporally-deformable alignment network for video super-resolution. In: Proceedings CVPR, pp. 3360\u20133369 (2020)","DOI":"10.1109\/CVPR42600.2020.00342"},{"key":"6_CR68","doi-asserted-by":"crossref","unstructured":"Tico, M., Gelfand, N., Pulli, K.: Motion-blur-free exposure fusion. In: Proceedings ICIP, pp. 3321\u20133324 (2010)","DOI":"10.1109\/ICIP.2010.5651532"},{"key":"6_CR69","doi-asserted-by":"crossref","unstructured":"Wang, X., Chan, K.C., Yu, K., Dong, C., Change Loy, C.: EDVR: video restoration with enhanced deformable convolutional networks. In: Proceedings CVPRW, pp. 1\u201310 (2019)","DOI":"10.1109\/CVPRW.2019.00247"},{"key":"6_CR70","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Progressive retinex: mutually reinforced illumination-noise perception network for low-light image enhancement. In: Proceedings ACM MM, pp. 2015\u20132023 (2019)","DOI":"10.1145\/3343031.3350983"},{"key":"6_CR71","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-030-58539-6_1","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Wang","year":"2020","unstructured":"Wang, Y., Huang, H., Xu, Q., Liu, J., Liu, Y., Wang, J.: Practical deep raw image denoising on\u00a0mobile devices. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12351, pp. 1\u201316. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58539-6_1"},{"issue":"4","key":"6_CR72","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. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"6_CR73","doi-asserted-by":"crossref","unstructured":"Wei, K., Fu, Y., Yang, J., Huang, H.: A physics-based noise formation model for extreme low-light raw denoising. In: Proceedings CVPR, pp. 2758\u20132767 (2020)","DOI":"10.1109\/CVPR42600.2020.00283"},{"key":"6_CR74","doi-asserted-by":"crossref","unstructured":"Whang, J., Delbracio, M., Talebi, H., Saharia, C., Dimakis, A.G., Milanfar, P.: Deblurring via stochastic refinement. In: Proceedings CVPR, pp. 16293\u201316303 (2022)","DOI":"10.1109\/CVPR52688.2022.01581"},{"issue":"2","key":"6_CR75","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1007\/s11263-011-0502-7","volume":"98","author":"O Whyte","year":"2012","unstructured":"Whyte, O., Sivic, J., Zisserman, A., Ponce, J.: Non-uniform deblurring for shaken images. Int. J. Comput. Vis. 98(2), 168\u2013186 (2012)","journal-title":"Int. J. Comput. Vis."},{"key":"6_CR76","doi-asserted-by":"crossref","unstructured":"Xia, Z., Perazzi, F., Gharbi, M., Sunkavalli, K., Chakrabarti, A.: Basis prediction networks for effective burst denoising with large kernels. In: Proceedings CVPR, pp. 11844\u201311853 (2020)","DOI":"10.1109\/CVPR42600.2020.01186"},{"key":"6_CR77","doi-asserted-by":"crossref","unstructured":"Xing, Y., Qian, Z., Chen, Q.: Invertible image signal processing. In: Proceedings CVPR, pp. 6287\u20136296 (2021)","DOI":"10.1109\/CVPR46437.2021.00622"},{"key":"6_CR78","unstructured":"Xu, X., Li, M., Sun, W.: Learning deformable kernels for image and video denoising. arXiv preprint arXiv:1904.06903 (2019)"},{"key":"6_CR79","doi-asserted-by":"crossref","unstructured":"Yoo, J., Ahn, N., Sohn, K.A.: Rethinking data augmentation for image super-resolution: A comprehensive analysis and a new strategy. In: Proceedings CVPR, pp. 8375\u20138384 (2020)","DOI":"10.1109\/CVPR42600.2020.00840"},{"key":"6_CR80","doi-asserted-by":"crossref","unstructured":"Yuan, L., Sun, J., Quan, L., Shum, H.Y.: Image deblurring with blurred\/noisy image pairs. ACM Trans. Graph. 26(3), 1-es (2007)","DOI":"10.1145\/1276377.1276379"},{"key":"6_CR81","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Su, W., Ma, D.: Efficient dynamic scene deblurring using spatially variant deconvolution network with optical flow guided training. In: Proceedings CVPR, pp. 3555\u20133564 (2020)","DOI":"10.1109\/CVPR42600.2020.00361"},{"key":"6_CR82","first-page":"1690","volume":"32","author":"Z Yue","year":"2019","unstructured":"Yue, Z., Yong, H., Zhao, Q., Meng, D., Zhang, L.: Variational denoising network: toward blind noise modeling and removal. Proc. NeurIPS 32, 1690\u20131701 (2019)","journal-title":"Proc. NeurIPS"},{"key":"6_CR83","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., et al.: CycleISP: real image restoration via improved data synthesis. In: Proceedings CVPR, pp. 2696\u20132705 (2020)","DOI":"10.1109\/CVPR42600.2020.00277"},{"key":"6_CR84","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1007\/978-3-030-58595-2_30","volume-title":"Computer Vision \u2013 ECCV 2020","author":"SW Zamir","year":"2020","unstructured":"Zamir, S.W., et al.: Learning enriched features for real image restoration and enhancement. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 492\u2013511. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58595-2_30"},{"key":"6_CR85","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., et al.: Multi-stage progressive image restoration. In: Proceedings CVPR, pp. 14821\u201314831 (2021)","DOI":"10.1109\/CVPR46437.2021.01458"},{"key":"6_CR86","doi-asserted-by":"crossref","unstructured":"Zhang, B., Jin, S., Xia, Y., Huang, Y., Xiong, Z.: Attention mechanism enhanced kernel prediction networks for denoising of burst images. In: Proceedings ICASSP, pp. 2083\u20132087 (2020)","DOI":"10.1109\/ICASSP40776.2020.9053332"},{"key":"6_CR87","doi-asserted-by":"crossref","unstructured":"Zhang, H., Dai, Y., Li, H., Koniusz, P.: Deep stacked hierarchical multi-patch network for image deblurring. In: Proceedings CVPR, pp. 5978\u20135986 (2019)","DOI":"10.1109\/CVPR.2019.00613"},{"key":"6_CR88","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Dynamic scene deblurring using spatially variant recurrent neural networks. In: Proceedings CVPR, pp. 2521\u20132529 (2018)","DOI":"10.1109\/CVPR.2018.00267"},{"key":"6_CR89","doi-asserted-by":"crossref","unstructured":"Zhang, J., Cao, Y., Fang, S., Kang, Y., Wen Chen, C.: Fast haze removal for nighttime image using maximum reflectance prior. In: Proceedings CVPR, pp. 7418\u20137426 (2017)","DOI":"10.1109\/CVPR.2017.742"},{"issue":"7","key":"6_CR90","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."},{"issue":"9","key":"6_CR91","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.: EFDNet: 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":"6_CR92","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, C., Maybank, S.J., Tao, D.: Exposure trajectory recovery from motion blur. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)","DOI":"10.1109\/TPAMI.2021.3116135"},{"issue":"7","key":"6_CR93","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."},{"key":"6_CR94","doi-asserted-by":"crossref","unstructured":"Zhu, X., Hu, H., Lin, S., Dai, J.: Deformable convnets v2: more deformable, better results. In: Proceedings CVPR, pp. 9308\u20139316 (2019)","DOI":"10.1109\/CVPR.2019.00953"}],"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\/978-3-031-20071-7_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T01:25:34Z","timestamp":1728350734000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20071-7_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200700","9783031200717"],"references-count":94,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20071-7_6","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":"13 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)"}}]}}