{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T22:25:16Z","timestamp":1757456716680,"version":"3.40.3"},"publisher-location":"Cham","reference-count":75,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030682378"},{"type":"electronic","value":"9783030682385"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-68238-5_29","type":"book-chapter","created":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T07:02:43Z","timestamp":1611990163000},"page":"379-397","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Deep Atrous Guided Filter for Image Restoration in Under Display Cameras"],"prefix":"10.1007","author":[{"given":"Varun","family":"Sundar","sequence":"first","affiliation":[]},{"given":"Sumanth","family":"Hegde","sequence":"additional","affiliation":[]},{"given":"Divya","family":"Kothandaraman","sequence":"additional","affiliation":[]},{"given":"Kaushik","family":"Mitra","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,31]]},"reference":[{"key":"29_CR1","unstructured":"Abdelhamed, A., Afifi, M., Timofte, R., Brown, M.S.: Ntire 2020 challenge on real image denoising: Dataset, methods and results. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2020"},{"key":"29_CR2","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R.: Ntire 2017 challenge on single image super-resolution: dataset and study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, July 2017","DOI":"10.1109\/CVPRW.2017.150"},{"key":"29_CR3","unstructured":"Ancuti, C.O., Ancuti, C., Vasluianu, F.A., Timofte, R.: Ntire 2020 challenge on nonhomogeneous dehazing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2020"},{"key":"29_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1007\/978-3-030-11021-5_21","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"Y Blau","year":"2019","unstructured":"Blau, Y., Mechrez, R., Timofte, R., Michaeli, T., Zelnik-Manor, L.: The 2018 PIRM Challenge on Perceptual Image Super-Resolution. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018, Part V. LNCS, vol. 11133, pp. 334\u2013355. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11021-5_21"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Brehm, S., Scherer, S., Lienhart, R.: High-resolution dual-stage multi-level feature aggregation for single image and video deblurring. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2020","DOI":"10.1109\/CVPRW50498.2020.00237"},{"key":"29_CR6","doi-asserted-by":"crossref","unstructured":"Chen, D., et al.: Gated context aggregation network for image dehazing and deraining. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, March 2018","DOI":"10.1109\/WACV.2019.00151"},{"issue":"6","key":"29_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2980179.2982423","volume":"35","author":"J Chen","year":"2016","unstructured":"Chen, J., Adams, A., Wadhwa, N., Hasinoff, S.W.: Bilateral guided upsampling. ACM Trans. Graph. (TOG) 35(6), 1\u20138 (2016)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"4","key":"29_CR8","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"29_CR9","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)"},{"key":"29_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L-C Chen","year":"2018","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018, Part VII. LNCS, vol. 11211, pp. 833\u2013851. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49"},{"key":"29_CR11","doi-asserted-by":"crossref","unstructured":"Chen, Q., Xu, J., Koltun, V.: Fast image processing with fully-convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), October 2017","DOI":"10.1109\/ICCV.2017.273"},{"key":"29_CR12","unstructured":"Chen, Y., Kalantidis, Y., Li, J., Yan, S., Feng, J.: A$$^{\\hat{}}$$ 2-nets: double attention networks. In: Advances in Neural Information Processing Systems, December 2018"},{"issue":"4","key":"29_CR13","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1145\/3072959.3073592","volume":"36","author":"M Gharbi","year":"2017","unstructured":"Gharbi, M., Chen, J., Barron, J.T., Hasinoff, S.W., Durand, F.: Deep bilateral learning for real-time image enhancement. ACM Trans. Graph. (TOG) 36(4), 118 (2017)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"29_CR14","unstructured":"Gong, E., Pauly, J., Zaharchuk, G.: Boosting SNR and\/or resolution of arterial spin label (ASL) imaging using multi-contrast approaches with multi-lateral guided filter and deep networks. In: Proceedings of the Annual Meeting of the International Society for Magnetic Resonance in Medicine, Honolulu, Hawaii (2017)"},{"key":"29_CR15","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, December 2014"},{"key":"29_CR16","doi-asserted-by":"crossref","unstructured":"Gu, S., Li, Y., Gool, L.V., Timofte, R.: Self-guided network for fast image denoising. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), October 2019","DOI":"10.1109\/ICCV.2019.00260"},{"key":"29_CR17","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.procs.2018.03.048","volume":"129","author":"Y Guo","year":"2018","unstructured":"Guo, Y., Han, S., Cao, H., Zhang, Y., Wang, Q.: Guided filter based deep recurrent neural networks for hyperspectral image classification. Procedia Computer Science 129, 219\u2013223 (2018)","journal-title":"Procedia Computer Science"},{"key":"29_CR18","doi-asserted-by":"crossref","unstructured":"Hamaguchi, R., Fujita, A., Nemoto, K., Imaizumi, T., Hikosaka, S.: Effective use of dilated convolutions for segmenting small object instances in remote sensing imagery. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, March 2018","DOI":"10.1109\/WACV.2018.00162"},{"issue":"6","key":"29_CR19","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1109\/TPAMI.2012.213","volume":"35","author":"K He","year":"2013","unstructured":"He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397\u20131409 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"29_CR20","unstructured":"He, K., Sun, J.: Fast guided filter. arXiv preprint arXiv:1505.00996 (2015)"},{"key":"29_CR21","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018","DOI":"10.1109\/CVPR.2018.00745"},{"key":"29_CR22","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)"},{"key":"29_CR23","unstructured":"Jiang, Y., et al.: Enlightengan: Deep light enhancement without paired supervision. arXiv preprint arXiv:1906.06972 (2019)"},{"key":"29_CR24","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, Part II. LNCS, vol. 9906, pp. 694\u2013711. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43"},{"key":"29_CR25","doi-asserted-by":"crossref","unstructured":"Kim, D.W., Ryun Chung, J., Jung, S.W.: GRDB: grouped residual dense network for real image denoising and GAN-based real-world noise modeling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2019","DOI":"10.1109\/CVPRW.2019.00261"},{"key":"29_CR26","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016","DOI":"10.1109\/CVPR.2016.182"},{"issue":"3","key":"29_CR27","doi-asserted-by":"publisher","first-page":"96-es","DOI":"10.1145\/1276377.1276497","volume":"26","author":"J Kopf","year":"2007","unstructured":"Kopf, J., Cohen, M.F., Lischinski, D., Uyttendaele, M.: Joint bilateral upsampling. ACM Trans. Graph. (ToG) 26(3), 96-es (2007)","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"29_CR28","doi-asserted-by":"crossref","unstructured":"Kupyn, O., Martyniuk, T., Wu, J., Wang, Z.: Deblurgan-v2: deblurring (orders-of-magnitude) faster and better. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), October 2019","DOI":"10.1109\/ICCV.2019.00897"},{"key":"29_CR29","unstructured":"Lehtinen, J., etal.: Noise2noise: learning image restoration without clean data. arXiv preprint arXiv:1803.04189 (2018)"},{"key":"29_CR30","doi-asserted-by":"crossref","unstructured":"Li, X., Wu, J., Lin, Z., Liu, H., Zha, H.: Recurrent squeeze-and-excitation context aggregation net for single image deraining. In: Proceedings of the European Conference on Computer Vision (ECCV) (September 2018)","DOI":"10.1007\/978-3-030-01234-2_16"},{"key":"29_CR31","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, July 2017","DOI":"10.1109\/CVPRW.2017.151"},{"key":"29_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1007\/978-3-030-01219-9_37","volume-title":"Computer Vision \u2013 ECCV 2018","author":"D Lin","year":"2018","unstructured":"Lin, D., Ji, Y., Lischinski, D., Cohen-Or, D., Huang, H.: Multi-scale context intertwining for semantic segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018, Part III. LNCS, vol. 11207, pp. 622\u2013638. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01219-9_37"},{"key":"29_CR33","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.106"},{"key":"29_CR34","unstructured":"Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations, April 2017"},{"key":"29_CR35","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations, April 2019"},{"key":"29_CR36","unstructured":"Lugmayr, A., Danelljan, M., Timofte, R.: Ntire 2020 challenge on real-world image super-resolution: Methods and results. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2020"},{"key":"29_CR37","unstructured":"Mao, X., Shen, C., Yang, Y.B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: Advances in Neural Information Processing Systems, December 2016"},{"key":"29_CR38","doi-asserted-by":"crossref","unstructured":"Marin, D., et al.: Efficient segmentation: learning downsampling near semantic boundaries. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), October 2019","DOI":"10.1109\/ICCV.2019.00222"},{"key":"29_CR39","unstructured":"Mei, Y., et al.: Pyramid attention networks for image restoration. arXiv preprint arXiv:2004.13824 (2020)"},{"key":"29_CR40","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1007\/978-3-030-11021-5_26","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"MMK Sarker","year":"2019","unstructured":"Sarker, M.M.K., Rashwan, H.A., Talavera, E., Banu, S.F., Radeva, P., Puig, D.: MACNet: multi-scale atrous convolution networks for food places classification in egocentric photo-streams. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018, Part V. LNCS, vol. 11133, pp. 423\u2013433. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11021-5_26"},{"key":"29_CR41","unstructured":"Nah, S., Son, S., Timofte, R., Lee, K.M.: Ntire 2020 challenge on image and video deblurring. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2020"},{"issue":"7","key":"29_CR42","doi-asserted-by":"publisher","first-page":"1593","DOI":"10.1364\/JOSAA.27.001593","volume":"27","author":"F Orieux","year":"2010","unstructured":"Orieux, F., Giovannelli, J.F., Rodet, T.: Bayesian estimation of regularization and point spread function parameters for wiener-hunt deconvolution. JOSA A 27(7), 1593\u20131607 (2010)","journal-title":"JOSA A"},{"key":"29_CR43","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, December 2019"},{"key":"29_CR44","doi-asserted-by":"crossref","unstructured":"Qin, X., Wang, Z., Bai, Y., Xie, X., Jia, H.: FFA-Net: feature fusion attention network for single image dehazing. In: AAAI, February 2020","DOI":"10.1609\/aaai.v34i07.6865"},{"key":"29_CR45","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015, Part III. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"29_CR46","doi-asserted-by":"crossref","unstructured":"Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016","DOI":"10.1109\/CVPR.2016.207"},{"key":"29_CR47","doi-asserted-by":"crossref","unstructured":"Sim, H., Kim, M.: A deep motion deblurring network based on per-pixel adaptive kernels with residual down-up and up-down modules. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2019","DOI":"10.1109\/CVPRW.2019.00267"},{"key":"29_CR48","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (ICLR), May 2015"},{"key":"29_CR49","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., Liu, X., Xu, C.: MemNet: A persistent memory network for image restoration. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), October 2017","DOI":"10.1109\/ICCV.2017.486"},{"key":"29_CR50","doi-asserted-by":"crossref","unstructured":"Tao, X., Gao, H., Shen, X., Wang, J., Jia, J.: Scale-recurrent network for deep image deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018","DOI":"10.1109\/CVPR.2018.00853"},{"key":"29_CR51","doi-asserted-by":"crossref","unstructured":"Timofte, R., Rothe, R., Van Gool, L.: Seven ways to improve example-based single image super resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016","DOI":"10.1109\/CVPR.2016.206"},{"key":"29_CR52","doi-asserted-by":"crossref","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.437"},{"key":"29_CR53","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018"},{"key":"29_CR54","doi-asserted-by":"crossref","unstructured":"Wang, P., et al.: Understanding convolution for semantic segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, March 2018","DOI":"10.1109\/WACV.2018.00163"},{"key":"29_CR55","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: Efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"29_CR56","doi-asserted-by":"crossref","unstructured":"Wang, Z., Ji, S.: Smoothed dilated convolutions for improved dense prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, August 2018","DOI":"10.1145\/3219819.3219944"},{"key":"29_CR57","unstructured":"Wenke, I.G.: Organic light emitting diode (OLED). Research gate (2016)"},{"key":"29_CR58","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018, Part VII. LNCS, vol. 11211, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1"},{"key":"29_CR59","doi-asserted-by":"crossref","unstructured":"Wu, H., Zheng, S., Zhang, J., Huang, K.: Fast end-to-end trainable guided filter. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018,","DOI":"10.1109\/CVPR.2018.00197"},{"issue":"1","key":"29_CR60","doi-asserted-by":"publisher","first-page":"144","DOI":"10.3390\/rs10010144","volume":"10","author":"Y Xu","year":"2018","unstructured":"Xu, Y., Wu, L., Xie, Z., Chen, Z.: Building extraction in very high resolution remote sensing imagery using deep learning and guided filters. Remote Sens. 10(1), 144 (2018)","journal-title":"Remote Sens."},{"key":"29_CR61","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: International Conference on Learning Representations (ICLR), May 2016"},{"key":"29_CR62","doi-asserted-by":"crossref","unstructured":"Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.75"},{"key":"29_CR63","doi-asserted-by":"crossref","unstructured":"Zhang, H., Patel, V.M.: Densely connected pyramid dehazing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018","DOI":"10.1109\/CVPR.2018.00337"},{"key":"29_CR64","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 (CVPR), June 2018","DOI":"10.1109\/CVPR.2018.00079"},{"key":"29_CR65","doi-asserted-by":"crossref","unstructured":"Zhang, J., Pan, J., Lai, W.S., Lau, R.W.H., Yang, M.H.: Learning fully convolutional networks for iterative non-blind deconvolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.737"},{"issue":"7","key":"29_CR66","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":"29_CR67","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 (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.300"},{"issue":"9","key":"29_CR68","doi-asserted-by":"publisher","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","volume":"27","author":"K Zhang","year":"2018","unstructured":"Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27(9), 4608\u20134622 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"29_CR69","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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018","DOI":"10.1109\/CVPR.2018.00068"},{"key":"29_CR70","doi-asserted-by":"crossref","unstructured":"Zhang, X., Chen, Q., Ng, R., Koltun, V.: Zoom to learn, learn to zoom. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.00388"},{"key":"29_CR71","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 1 (2020). https:\/\/ieeexplore.ieee.org\/document\/8964437"},{"issue":"1","key":"29_CR72","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","volume":"3","author":"H Zhao","year":"2016","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 (2016)","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"29_CR73","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.660"},{"key":"29_CR74","unstructured":"Zhou, Y., et al.: UDC 2020 challenge on image restoration of under-display camera: Methods and results. arXiv preprint arXiv:2008.07742 (2020)"},{"key":"29_CR75","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Ren, D., Emerton, N., Lim, S., Large, T.: Image restoration for under-display camera. arXiv preprint arXiv:2003.04857 (2020)","DOI":"10.1109\/CVPR46437.2021.00906"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68238-5_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T23:06:55Z","timestamp":1738192015000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-68238-5_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030682378","9783030682385"],"references-count":75,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68238-5_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"31 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1360","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}