{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/semi-supervised-cnn-for-single-image-rain","title":"Semi-supervised Transfer Learning for Image Rain Removal","arxiv_id":"1807.11078","date":"2018-07-29","proceeding":"CVPR 2019 6","authors":["Wei Wei","Deyu Meng","Qian Zhao","Zongben Xu","Ying Wu"],"abstract":"Single image rain removal is a typical inverse problem in computer vision.\nThe deep learning technique has been verified to be effective for this task and\nachieved state-of-the-art performance. However, previous deep learning methods\nneed to pre-collect a large set of image pairs with/without synthesized rain\nfor training, which tends to make the neural network be biased toward learning\nthe specific patterns of the synthesized rain, while be less able to generalize\nto real test samples whose rain types differ from those in the training data.\nTo this issue, this paper firstly proposes a semi-supervised learning paradigm\ntoward this task. Different from traditional deep learning methods which only\nuse supervised image pairs with/without synthesized rain, we further put real\nrainy images, without need of their clean ones, into the network training\nprocess. This is realized by elaborately formulating the residual between an\ninput rainy image and its expected network output (clear image without rain) as\na specific parametrized rain streaks distribution. The network is therefore\ntrained to adapt real unsupervised diverse rain types through transferring from\nthe supervised synthesized rain, and thus both the short-of-training-sample and\nbias-to-supervised-sample issues can be evidently alleviated. Experiments on\nsynthetic and real data verify the superiority of our model compared to the\nstate-of-the-arts.","url_abs":"http://arxiv.org/abs/1807.11078v2","url_pdf":"http://arxiv.org/pdf/1807.11078v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"semi-supervised-cnn-for-single-image-rain","repo_url":"https://github.com/wwzjer/Semi-supervised-IRR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"rain-removal","task_name":"Rain Removal"},{"task_slug":"single-image-deraining","task_name":"Single Image Deraining"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/single-image-deraining-on-rain100h","task":"Single Image Deraining","dataset":"Rain100H","model":"SEMI","rank_in_archive_order":15,"of":19,"metrics":{"PSNR":"16.56","SSIM":"0.486"},"uses_additional_data":false},{"leaderboard":"/sota/single-image-deraining-on-rain100l","task":"Single Image Deraining","dataset":"Rain100L","model":"SEMI","rank_in_archive_order":14,"of":19,"metrics":{"PSNR":"25.03","SSIM":"0.842"},"uses_additional_data":false},{"leaderboard":"/sota/single-image-deraining-on-test100","task":"Single Image Deraining","dataset":"Test100","model":"SEMI","rank_in_archive_order":8,"of":12,"metrics":{"PSNR":"22.35","SSIM":"0.788"},"uses_additional_data":false},{"leaderboard":"/sota/single-image-deraining-on-test1200","task":"Single Image Deraining","dataset":"Test1200","model":"SEMI","rank_in_archive_order":9,"of":14,"metrics":{"PSNR":"26.05","SSIM":"0.822"},"uses_additional_data":false},{"leaderboard":"/sota/single-image-deraining-on-test2800","task":"Single Image Deraining","dataset":"Test2800","model":"SEMI","rank_in_archive_order":10,"of":12,"metrics":{"PSNR":"24.43","SSIM":"0.782"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}