{"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/pms-net-robust-haze-removal-based-on-patch","title":"PMS-Net: Robust Haze Removal Based on Patch Map for Single Images","arxiv_id":null,"date":"2019-06-01","proceeding":"CVPR 2019 6","authors":["Wei-Ting Chen"," Jian-Jiun Ding"," Sy-Yen Kuo"],"abstract":"In this paper, we proposed a novel haze removal algorithm based on a new feature called the patch map. Conventional patch-based haze removal algorithms (e.g. the Dark Channel prior) usually performs dehazing with a fixed patch size. However, it may produce several problems in recovered results such as oversaturation and color distortion. Therefore, in this paper, we designed an adaptive and automatic patch size selection model called the Patch Map Selection Network (PMS-Net) to select the patch size corresponding to each pixel. This network is designed based on the convolutional neural network (CNN), which can generate the patch map from the image to image. Experimental results on both synthesized and real-world hazy images show that, with the combination of the proposed PMS-Net, the performance in haze removal is much better than that of other state-of-the-art algorithms and we can address the problems caused by the fixed patch size.\r","url_abs":"http://openaccess.thecvf.com/content_CVPR_2019/html/Chen_PMS-Net_Robust_Haze_Removal_Based_on_Patch_Map_for_Single_CVPR_2019_paper.html","url_pdf":"http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_PMS-Net_Robust_Haze_Removal_Based_on_Patch_Map_for_Single_CVPR_2019_paper.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":"pms-net-robust-haze-removal-based-on-patch","repo_url":"https://github.com/weitingchen83/PMS-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"computational-phenotyping","task_name":"Computational Phenotyping"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-dehazing","task_name":"Image Dehazing"},{"task_slug":"image-restoration","task_name":"Image Restoration"},{"task_slug":"nonhomogeneous-image-dehazing","task_name":"Nonhomogeneous Image Dehazing"},{"task_slug":"rain-removal","task_name":"Rain Removal"},{"task_slug":"single-image-dehazing","task_name":"Single Image Dehazing"},{"task_slug":"single-image-deraining","task_name":"Single Image Deraining"},{"task_slug":"single-image-haze-removal","task_name":"Single Image Haze Removal"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}