{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T07:18:44Z","timestamp":1775200724621,"version":"3.50.1"},"reference-count":80,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T00:00:00Z","timestamp":1649203200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T00:00:00Z","timestamp":1649203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000083","name":"directorate for computer and information science and engineering","doi-asserted-by":"publisher","award":["CAREER 1149783"],"award-info":[{"award-number":["CAREER 1149783"]}],"id":[{"id":"10.13039\/100000083","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s11263-022-01583-y","type":"journal-article","created":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T06:08:15Z","timestamp":1649225295000},"page":"1440-1458","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Dual Convolutional Neural Networks for Low-Level Vision"],"prefix":"10.1007","volume":"130","author":[{"given":"Jinshan","family":"Pan","sequence":"first","affiliation":[]},{"given":"Deqing","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Jiawei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jinhui","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yu-Wing","family":"Tai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4848-2304","authenticated-orcid":false,"given":"Ming-Hsuan","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,6]]},"reference":[{"key":"1583_CR1","doi-asserted-by":"crossref","unstructured":"Berman, D., Treibitz, T., & Avidan, S. (2016). Non-local image dehazing. In CVPR (pp. 1674\u20131682).","DOI":"10.1109\/CVPR.2016.185"},{"key":"1583_CR2","doi-asserted-by":"crossref","unstructured":"Bulat, A., Yang, J., & Tzimiropoulos, G. (2018). To learn image super-resolution, use a GAN to learn how to do image degradation first. In ECCV (pp. 187\u2013202).","DOI":"10.1007\/978-3-030-01231-1_12"},{"key":"1583_CR3","doi-asserted-by":"crossref","unstructured":"Burger, H. C., Schuler, C. J., & Harmeling, S. (2012). Image denoising: Can plain neural networks compete with bm3d? In CVPR (pp. 2392\u20132399).","DOI":"10.1109\/CVPR.2012.6247952"},{"key":"1583_CR4","doi-asserted-by":"crossref","unstructured":"Burger, H., Schuler, C., & Harmeling, S. (2012). Image denosing: Can plain neural networks compete with BM3D. In CVPR.","DOI":"10.1109\/CVPR.2012.6247952"},{"issue":"11","key":"1583_CR5","first-page":"5187","volume":"25","author":"B Cai","year":"2016","unstructured":"Cai, B., Xu, X., Jia, K., Qing, C., & Tao, D. (2016). Dehazenet: An end-to-end system for single image haze removal. IEEE TIP, 25(11), 5187\u20135198.","journal-title":"IEEE TIP"},{"key":"1583_CR6","doi-asserted-by":"crossref","unstructured":"Chen, D., & Davies, M. E. (2020). Deep decomposition learning for inverse imaging problems. In ECCV (pp. 510\u2013526).","DOI":"10.1007\/978-3-030-58604-1_31"},{"key":"1583_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Y. L., & Hsu, C. T. (2013). A generalized low-rank appearance model for spatio-temporally correlated rain streaks. In ICCV (pp. 1968\u20131975).","DOI":"10.1109\/ICCV.2013.247"},{"key":"1583_CR8","doi-asserted-by":"crossref","unstructured":"Chen, Q., Xu, J., & Koltun, V. (2017). Fast image processing with fully-convolutional networks. In ICCV (pp. 2516\u20132525).","DOI":"10.1109\/ICCV.2017.273"},{"issue":"8","key":"1583_CR9","first-page":"2080","volume":"16","author":"K Dabov","year":"2007","unstructured":"Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. O. (2007). Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE TIP, 16(8), 2080\u20132095.","journal-title":"IEEE TIP"},{"key":"1583_CR10","doi-asserted-by":"crossref","unstructured":"Dong, C., Deng, Y., Loy, C. C., & Tang, X. (2015). Compression artifacts reduction by a deep convolutional network. In ICCV (pp. 576\u2013584).","DOI":"10.1109\/ICCV.2015.73"},{"key":"1583_CR11","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C. C., & Tang, X. (2016). Accelerating the super-resolution convolutional neural network. In ECCV (pp. 391\u2013407).","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"1583_CR12","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C. C., He, K., & Tang, X. (2014). Learning a deep convolutional network for image super-resolution. In ECCV (pp. 184\u2013199).","DOI":"10.1007\/978-3-319-10593-2_13"},{"issue":"2","key":"1583_CR13","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2016","unstructured":"Dong, C., Loy, C. C., He, K., & Tang, X. (2016). Image super-resolution using deep convolutional networks. IEEE TPAMI, 38(2), 295\u2013307.","journal-title":"IEEE TPAMI"},{"key":"1583_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TPAMI.2021.3138787","volume":"52","author":"J Dong","year":"2021","unstructured":"Dong, J., Roth, S., & Schiele, B. (2021). DWDN: Deep wiener deconvolution network for non-blind image deblurring. IEEE TPAMI, 52, 1. https:\/\/doi.org\/10.1109\/TPAMI.2021.3138787.","journal-title":"IEEE TPAMI"},{"key":"1583_CR15","doi-asserted-by":"crossref","unstructured":"Eigen, D., Krishnan, D., & Fergus, R. (2013). Restoring an image taken through a window covered with dirt or rain. In ICCV (pp. 633\u2013640).","DOI":"10.1109\/ICCV.2013.84"},{"key":"1583_CR16","doi-asserted-by":"crossref","unstructured":"Fan, Q., Chen, D., Yuan, L., Hua, G., Yu, N., & Chen, B. (2018a). Decouple learning for parameterized image operators. In ECCV (pp. 455\u2013471).","DOI":"10.1007\/978-3-030-01261-8_27"},{"key":"1583_CR17","doi-asserted-by":"crossref","unstructured":"Fan, Q., Yang, J., Wipf, D. P., Chen, B., & Tong, X. (2018b). Image smoothing via unsupervised learning. ACM TOG, 37(6), 259:1-259:14.","DOI":"10.1145\/3272127.3275081"},{"key":"1583_CR18","doi-asserted-by":"crossref","unstructured":"Fu, X., Huang, J., Zeng, D., Huang, Y., Ding, X., & Paisley, J. (2017). Removing rain from single images via a deep detail network. In CVPR (pp. 3855\u20133863).","DOI":"10.1109\/CVPR.2017.186"},{"issue":"6","key":"1583_CR19","doi-asserted-by":"publisher","first-page":"2944","DOI":"10.1109\/TIP.2017.2691802","volume":"26","author":"X Fu","year":"2017","unstructured":"Fu, X., Huang, J., Ding, X., Liao, Y., & Paisley, J. (2017). Clearing the skies: A deep network architecture for single-image rain removal. IEEE Transactions on Image Processing, 26(6), 2944\u20132956.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1583_CR20","doi-asserted-by":"crossref","unstructured":"Girshick, R. B. (2015). Fast R-CNN. In ICCV (pp. 1440\u20131448).","DOI":"10.1109\/ICCV.2015.169"},{"key":"1583_CR21","doi-asserted-by":"crossref","unstructured":"Guo, T., Li, X., Cherukuri, V., & Monga, V. (2019). Dense scene information estimation network for dehazing. In CVPR workshops (pp. 2122\u20132130).","DOI":"10.1109\/CVPRW.2019.00265"},{"key":"1583_CR22","doi-asserted-by":"crossref","unstructured":"Haris, M., Shakhnarovich, G., & Ukita, N. (2018). Deep back-projection networks for super-resolution. In CVPR (pp. 1664\u20131673).","DOI":"10.1109\/CVPR.2018.00179"},{"key":"1583_CR23","unstructured":"He, K., Sun, J., & Tang, X. (2009). Single image haze removal using dark channel prior. In CVPR (pp. 1956\u20131963)."},{"key":"1583_CR24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In CVPR (pp. 770\u2013778).","DOI":"10.1109\/CVPR.2016.90"},{"key":"1583_CR25","doi-asserted-by":"crossref","unstructured":"Huang, J. B., Singh, A., & Ahuja, N. (2015). Single image super-resolution from transformed self-exemplars. In CVPR (pp. 5197\u20135206).","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"1583_CR26","doi-asserted-by":"crossref","unstructured":"Isobe, T., Jia, X., Gu, S., Li, S., Wang, S., & Tian, Q. (2020). Video super-resolution with recurrent structure-detail network. In ECCV (pp. 645\u2013660).","DOI":"10.1007\/978-3-030-58610-2_38"},{"key":"1583_CR27","unstructured":"Jain, V., & Seung, H. S. (2008). Natural image denoising with convolutional networks. In NIPS (pp. 769\u2013776)."},{"issue":"4","key":"1583_CR28","first-page":"1742","volume":"21","author":"LW Kang","year":"2012","unstructured":"Kang, L. W., Lin, C. W., & Fu, Y. H. (2012). Automatic single-image-based rain streaks removal via image decomposition. IEEE TIP, 21(4), 1742\u20131755.","journal-title":"IEEE TIP"},{"key":"1583_CR29","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J. K., & Lee, K. M. (2016). Accurate image super-resolution using very deep convolutional networks. In CVPR (pp. 1646\u20131654).","DOI":"10.1109\/CVPR.2016.182"},{"key":"1583_CR30","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J. K., & Lee, K. M. (2016). Deeply-recursive convolutional network for image super-resolution. In CVPR (pp. 1637\u20131645).","DOI":"10.1109\/CVPR.2016.181"},{"key":"1583_CR31","unstructured":"Krishnan, D., & Fergus, R. (2009). Fast image deconvolution using hyper-Laplacian priors. In NIPS (pp. 1033\u20131041)."},{"key":"1583_CR32","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In NIPS (pp. 1106\u20131114)."},{"issue":"11","key":"1583_CR33","doi-asserted-by":"publisher","first-page":"2599","DOI":"10.1109\/TPAMI.2018.2865304","volume":"41","author":"WS Lai","year":"2019","unstructured":"Lai, W. S., Huang, J. B., Ahuja, N., & Yang, M. H. (2019). Fast and accurate image super-resolution with deep Laplacian pyramid networks. IEEE TPAMI, 41(11), 2599\u20132613.","journal-title":"IEEE TPAMI"},{"key":"1583_CR34","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., & Shi, W. (2017). Photo-realistic single image super-resolution using a generative adversarial network. In CVPR (pp. 4681\u20134690).","DOI":"10.1109\/CVPR.2017.19"},{"key":"1583_CR35","doi-asserted-by":"crossref","unstructured":"Levin, A., Weiss, Y., Durand, F., & Freeman, W. T. (2009). Understanding and evaluating blind deconvolution algorithms. In CVPR (pp. 1964\u20131971).","DOI":"10.1109\/CVPR.2009.5206815"},{"issue":"3","key":"1583_CR36","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1145\/1276377.1276464","volume":"26","author":"A Levin","year":"2007","unstructured":"Levin, A., Fergus, R., Durand, F., & Freeman, W. T. (2007). Image and depth from a conventional camera with a coded aperture. ACM TOG, 26(3), 70.","journal-title":"ACM TOG"},{"key":"1583_CR37","doi-asserted-by":"crossref","unstructured":"Li, R., Pan, J., Li, Z., & Tang, J. (2018). Single image dehazing via conditional generative adversarial network. In CVPR (pp. 8202\u20138211).","DOI":"10.1109\/CVPR.2018.00856"},{"key":"1583_CR38","doi-asserted-by":"crossref","unstructured":"Li, B., Peng, X., Wang, Z., Xu, J., & Feng, D. (2017). Aod-net: all-in-one dehazing network. In ICCV (pp. 4780\u20134788).","DOI":"10.1109\/ICCV.2017.511"},{"key":"1583_CR39","doi-asserted-by":"crossref","unstructured":"Li, Y., Tan, R.T., Guo, X., Lu, J., & Brown, M. S. (2016). Rain streak removal using layer priors. In CVPR (pp. 2736\u20132744).","DOI":"10.1109\/CVPR.2016.299"},{"key":"1583_CR40","doi-asserted-by":"crossref","unstructured":"Liao, R., Tao, X., Li, R., Ma, Z., & Jia, J. (2015). Video super-resolution via deep draft-ensemble learning. In ICCV (pp. 531\u2013539).","DOI":"10.1109\/ICCV.2015.68"},{"key":"1583_CR41","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., & Lee, K. M. (2017). Enhanced deep residual networks for single image super-resolution. In CVPR workshop (pp. 1132\u20131140).","DOI":"10.1109\/CVPRW.2017.151"},{"key":"1583_CR42","doi-asserted-by":"crossref","unstructured":"Lin, T. Y., Roy Chowdhury, A., & Maji, S. (2015). Bilinear CNN models for fine-grained visual recognition. In ICCV (pp. 1449\u20131457).","DOI":"10.1109\/ICCV.2015.170"},{"key":"1583_CR43","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.cviu.2019.05.003","volume":"186","author":"S Li","year":"2019","unstructured":"Li, S., Ren, W., Zhang, J., Yu, J., & Guo, X. (2019). Single image rain removal via a deep decomposition-composition network. Computer Vision Image Understanding, 186, 48\u201357.","journal-title":"Computer Vision Image Understanding"},{"key":"1583_CR44","doi-asserted-by":"crossref","unstructured":"Liu, S., Pan, J., & Yang, M. H. (2016). Learning recursive filters for low-level vision via a hybrid neural network. In ECCV (pp. 560\u2013576).","DOI":"10.1007\/978-3-319-46493-0_34"},{"key":"1583_CR45","doi-asserted-by":"crossref","unstructured":"Martin, D. R., Fowlkes, C. C., Tal, D., & Malik, J. (2001). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV (pp. 416\u2013425).","DOI":"10.1109\/ICCV.2001.937655"},{"key":"1583_CR46","doi-asserted-by":"crossref","unstructured":"Meng, G., Wang, Y., Duan, J., Xiang, S., & Pan, C. (2013). Efficient image dehazing with boundary constraint and contextual regularization. In ICCV (pp. 617\u2013624).","DOI":"10.1109\/ICCV.2013.82"},{"key":"1583_CR47","doi-asserted-by":"crossref","unstructured":"Pan, J., Liu, S., Sun, D., Zhang, J., Liu, Y., Ren, J., Li, Z., Tang, J., Lu, H., Tai, Y. W., & Yang, M. H. (2018). Learning dual convolutional neural networks for low-level vision. In CVPR (pp. 3070\u20133079).","DOI":"10.1109\/CVPR.2018.00324"},{"issue":"7","key":"1583_CR48","doi-asserted-by":"publisher","first-page":"2449","DOI":"10.1109\/TPAMI.2020.2969348","volume":"43","author":"J Pan","year":"2021","unstructured":"Pan, J., Dong, J., Liu, Y., Zhang, J., Ren, J. S. J., Tang, J., et al. (2021). Physics-based generative adversarial models for image restoration and beyond. IEEE TPAMI, 43(7), 2449\u20132462.","journal-title":"IEEE TPAMI"},{"issue":"10","key":"1583_CR49","doi-asserted-by":"publisher","first-page":"2315","DOI":"10.1109\/TPAMI.2017.2753804","volume":"40","author":"J Pan","year":"2018","unstructured":"Pan, J., Sun, D., Pfister, H., & Yang, M. (2018). Deblurring images via dark channel prior. IEEE TPAMI, 40(10), 2315\u20132328.","journal-title":"IEEE TPAMI"},{"key":"1583_CR50","doi-asserted-by":"crossref","unstructured":"Qian, R., Tan, R. T., Yang, W., Su, J., & Liu, J. (2018). Attentive generative adversarial network for raindrop removal from a single image. In CVPR (pp. 2482\u20132491).","DOI":"10.1109\/CVPR.2018.00263"},{"key":"1583_CR51","doi-asserted-by":"crossref","unstructured":"Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., & Yang, M. H. (2016). Single image dehazing via multi-scale convolutional neural networks. In ECCV (pp. 154\u2013169).","DOI":"10.1007\/978-3-319-46475-6_10"},{"key":"1583_CR52","unstructured":"Ren, J. S. J., Xu, L., Yan, Q., & Sun, W. (2015). Shepard convolutional neural networks. In NIPS (pp. 901\u2013909)."},{"issue":"5","key":"1583_CR53","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1109\/TPAMI.2008.132","volume":"31","author":"A Saxena","year":"2009","unstructured":"Saxena, A., Sun, M., & Ng, A. Y. (2009). Make3d: Learning 3d scene structure from a single still image. IEEE TPAMI, 31(5), 824\u2013840.","journal-title":"IEEE TPAMI"},{"key":"1583_CR54","doi-asserted-by":"crossref","unstructured":"Schmidt, U., & Roth, S. (2014). Shrinkage fields for effective image restoration. In CVPR (pp. 2774\u20132781).","DOI":"10.1109\/CVPR.2014.349"},{"key":"1583_CR55","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., & Wang, Z. (2016). Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In CVPR (pp. 1874\u20131883).","DOI":"10.1109\/CVPR.2016.207"},{"key":"1583_CR56","doi-asserted-by":"crossref","unstructured":"Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012). Indoor segmentation and support inference from RGBD images. In ECCV (pp. 746\u2013760).","DOI":"10.1007\/978-3-642-33715-4_54"},{"key":"1583_CR57","doi-asserted-by":"publisher","first-page":"103034","DOI":"10.1016\/j.cviu.2020.103034","volume":"199","author":"V Singh","year":"2020","unstructured":"Singh, V., Ramnath, K., & Mittal, A. (2020). Refining high-frequencies for sharper super-resolution and deblurring. Computer Vision Image Understanding, 199, 103034.","journal-title":"Computer Vision Image Understanding"},{"key":"1583_CR58","unstructured":"Sun, Y., Chen, Y., Wang, X., & Tang, X. (2014). Deep learning face representation by joint identification-verification. In NIPS (pp. 1988\u20131996)."},{"issue":"2","key":"1583_CR59","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1109\/MITS.2012.2189969","volume":"4","author":"J Tarel","year":"2012","unstructured":"Tarel, J., Hauti\u00e8re, N., Caraffa, L., Cord, A., Halmaoui, H., & Gruyer, D. (2012). Vision enhancement in homogeneous and heterogeneous fog. IEEE Intelligent Transportation Systems Magazine, 4(2), 6\u201320.","journal-title":"IEEE Intelligent Transportation Systems Magazine"},{"key":"1583_CR60","doi-asserted-by":"crossref","unstructured":"Tian, C., Xu, Y., Zuo, W., Du, B., Lin, C. W., & Zhang, D. (2020). Designing and training of A dual CNN for image denoising. CoRR arXiv:2007.03951","DOI":"10.1016\/j.knosys.2021.106949"},{"key":"1583_CR61","doi-asserted-by":"crossref","unstructured":"Timofte, R., Smet, V. D., & Gool, L. J. V. (2014). A+: Adjusted anchored neighborhood regression for fast super-resolution. In ACCV (pp. 111\u2013126).","DOI":"10.1007\/978-3-319-16817-3_8"},{"key":"1583_CR62","unstructured":"Xie, J., Xu, L., & Chen, E. (2012). Image denoising and inpainting with deep neural networks. In NIPS (pp. 350\u2013358)."},{"key":"1583_CR63","unstructured":"Xu, L., Ren, J. S. J., Liu, C., & Jia, J. (2014). Deep convolutional neural network for image deconvolution. In NIPS (pp. 1790\u20131798)."},{"key":"1583_CR64","unstructured":"Xu, L., Ren, J.S.J., Yan, Q., Liao, R., & Jia, J. (2015). Deep edge-aware filters. In ICML (pp. 1669\u20131678)."},{"issue":"6","key":"1583_CR65","first-page":"174:1","volume":"30","author":"L Xu","year":"2011","unstructured":"Xu, L., Lu, C., Xu, Y., & Jia, J. (2011). Image smoothing via $$L _{{0}}$$ gradient minimization. ACM TOG, 30(6), 174:1-174:12.","journal-title":"ACM TOG"},{"issue":"6","key":"1583_CR66","first-page":"139:1","volume":"31","author":"L Xu","year":"2012","unstructured":"Xu, L., Yan, Q., Xia, Y., & Jia, J. (2012). Structure extraction from texture via relative total variation. ACM TOG, 31(6), 139:1-139:10.","journal-title":"ACM TOG"},{"key":"1583_CR67","unstructured":"Yang, H., Pan, J., Yan, Q., Sun, W., Ren, J. S. J., & Tai, Y. W. (2017). Image dehazing using bilinear composition loss function. CoRR arXiv:1710.00279"},{"key":"1583_CR68","doi-asserted-by":"crossref","unstructured":"Yang, A., Wang, H., Ji, Z., Pang, Y., & Shao, L. (2019). Dual-path in dual-path network for single image dehazing. In IJCAI (pp. 4627\u20134634).","DOI":"10.24963\/ijcai.2019\/643"},{"key":"1583_CR69","doi-asserted-by":"crossref","unstructured":"Zhang, H., & Patel, V. M. (2018). Densely connected pyramid dehazing network. In CVPR (pp. 3194\u20133203).","DOI":"10.1109\/CVPR.2018.00337"},{"key":"1583_CR70","doi-asserted-by":"crossref","unstructured":"Zhang, H., & Patel, V. M. (2018). Density-aware single image de-raining using a multi-stream dense network. In CVPR (pp. 695\u2013704).","DOI":"10.1109\/CVPR.2018.00079"},{"key":"1583_CR71","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., & Fu, Y. (2018). Image super-resolution using very deep residual channel attention networks. In ECCV (pp. 294\u2013310).","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"1583_CR72","doi-asserted-by":"crossref","unstructured":"Zhang, J., Pan, J., Lai, W. S., Lau, R. W. H., & Yang, M. H. (2017). Learning fully convolutional networks for iterative non-blind deconvolution. In CVPR (pp. 6969\u20136977).","DOI":"10.1109\/CVPR.2017.737"},{"key":"1583_CR73","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., & Fu, Y. (2018). Residual dense network for image super-resolution. In CVPR (pp. 2472\u20132481).","DOI":"10.1109\/CVPR.2018.00262"},{"key":"1583_CR74","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Xu, L., & Jia, J. (2014). 100+ times faster weighted median filter (WMF). In CVPR (pp. 2830\u20132837).","DOI":"10.1109\/CVPR.2014.362"},{"key":"1583_CR75","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Gu, S., & Zhang, L. (2017). Learning deep CNN denoiser prior for image restoration. In CVPR (pp. 2808\u20132817).","DOI":"10.1109\/CVPR.2017.300"},{"issue":"11","key":"1583_CR76","first-page":"3943","volume":"30","author":"H Zhang","year":"2020","unstructured":"Zhang, H., Sindagi, V., & Patel, V. M. (2020). Image de-raining using a conditional generative adversarial network. IEEE TCSVT, 30(11), 3943\u20133956.","journal-title":"IEEE TCSVT"},{"issue":"7","key":"1583_CR77","first-page":"3142","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017). Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE TIP, 26(7), 3142\u20133155.","journal-title":"IEEE TIP"},{"key":"1583_CR78","doi-asserted-by":"crossref","unstructured":"Zhu, H., Peng, X., Chandrasekhar, V., Li, L., & Lim, J. H. (2018). Dehazegan: When image dehazing meets differential programming. In IJCAI (pp. 1234\u20131240).","DOI":"10.24963\/ijcai.2018\/172"},{"issue":"2","key":"1583_CR79","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1109\/TCYB.2019.2955092","volume":"51","author":"H Zhu","year":"2021","unstructured":"Zhu, H., Cheng, Y., Peng, X., Zhou, J. T., Kang, Z., Lu, S., et al. (2021). Single-image dehazing via compositional adversarial network. IEEE Transactions on Cybernetics, 51(2), 829\u2013838.","journal-title":"IEEE Transactions on Cybernetics"},{"key":"1583_CR80","doi-asserted-by":"crossref","unstructured":"Zoran, D., & Weiss, Y. (2011). From learning models of natural image patches to whole image restoration. In ICCV (pp. 479\u2013486).","DOI":"10.1109\/ICCV.2011.6126278"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-022-01583-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-022-01583-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-022-01583-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T21:07:54Z","timestamp":1653685674000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-022-01583-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,6]]},"references-count":80,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["1583"],"URL":"https:\/\/doi.org\/10.1007\/s11263-022-01583-y","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,6]]},"assertion":[{"value":"11 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}