{"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/density-aware-single-image-de-raining-using-a","title":"Density-aware Single Image De-raining using a Multi-stream Dense Network","arxiv_id":"1802.07412","date":"2018-02-21","proceeding":"CVPR 2018 6","authors":["He Zhang","Vishal M. Patel"],"abstract":"Single image rain streak removal is an extremely challenging problem due to\nthe presence of non-uniform rain densities in images. We present a novel\ndensity-aware multi-stream densely connected convolutional neural network-based\nalgorithm, called DID-MDN, for joint rain density estimation and de-raining.\nThe proposed method enables the network itself to automatically determine the\nrain-density information and then efficiently remove the corresponding\nrain-streaks guided by the estimated rain-density label. To better characterize\nrain-streaks with different scales and shapes, a multi-stream densely connected\nde-raining network is proposed which efficiently leverages features from\ndifferent scales. Furthermore, a new dataset containing images with\nrain-density labels is created and used to train the proposed density-aware\nnetwork. Extensive experiments on synthetic and real datasets demonstrate that\nthe proposed method achieves significant improvements over the recent\nstate-of-the-art methods. In addition, an ablation study is performed to\ndemonstrate the improvements obtained by different modules in the proposed\nmethod. Code can be found at: https://github.com/hezhangsprinter","url_abs":"http://arxiv.org/abs/1802.07412v1","url_pdf":"http://arxiv.org/pdf/1802.07412v1.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":"density-aware-single-image-de-raining-using-a","repo_url":"https://github.com/hezhangsprinter/DID-MDN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"density-estimation","task_name":"Density Estimation"},{"task_slug":"single-image-deraining","task_name":"Single Image Deraining"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/single-image-deraining-on-rain100h","task":"Single Image Deraining","dataset":"Rain100H","model":"DIDMDN","rank_in_archive_order":19,"of":19,"metrics":{"SSIM":"0.524"},"uses_additional_data":false},{"leaderboard":"/sota/single-image-deraining-on-rain100l","task":"Single Image Deraining","dataset":"Rain100L","model":"DIDMDN","rank_in_archive_order":19,"of":19,"metrics":{"SSIM":"0.741"},"uses_additional_data":false},{"leaderboard":"/sota/single-image-deraining-on-raincityscapes","task":"Single Image Deraining","dataset":"RainCityscapes","model":"DID-MDN","rank_in_archive_order":6,"of":6,"metrics":{"PSNR":"28.43","SSIM":"0.9349"},"uses_additional_data":false},{"leaderboard":"/sota/single-image-deraining-on-test100","task":"Single Image Deraining","dataset":"Test100","model":"DIDMDN","rank_in_archive_order":11,"of":12,"metrics":{"SSIM":"0.818"},"uses_additional_data":false},{"leaderboard":"/sota/single-image-deraining-on-test1200","task":"Single Image Deraining","dataset":"Test1200","model":"DIDMDN","rank_in_archive_order":12,"of":14,"metrics":{"SSIM":"0.901"},"uses_additional_data":false},{"leaderboard":"/sota/single-image-deraining-on-test2800","task":"Single Image Deraining","dataset":"Test2800","model":"DIDMDN","rank_in_archive_order":9,"of":12,"metrics":{"PSNR":"28.13","SSIM":"0.867"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.07412","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}