{"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/mop-moire-patterns-using-mopnet","title":"Mop Moire Patterns Using MopNet","arxiv_id":null,"date":"2019-10-01","proceeding":"ICCV 2019 10","authors":["Bin He"," Ce Wang"," Boxin Shi"," Ling-Yu Duan"],"abstract":"Moire pattern is a common image quality degradation caused by frequency aliasing between monitors and cameras when taking screen-shot photos. The complex frequency distribution, imbalanced magnitude in colour channels, and diverse appearance attributes of moire pattern make its removal a challenging problem. In this paper, we propose a Moire pattern Removal Neural Network (MopNet) to solve this problem. All core components of MopNet are specially designed for unique properties of moire patterns, including the multi-scale feature aggregation addressing complex frequency, the channel-wise target edge predictor to exploit imbalanced magnitude among colour channels, and the attribute-aware classifier to characterize the diverse appearance for better modelling Moire patterns. Quantitative and qualitative experimental comparison validate the state-of-the-art performance of MopNet.\r","url_abs":"http://openaccess.thecvf.com/content_ICCV_2019/html/He_Mop_Moire_Patterns_Using_MopNet_ICCV_2019_paper.html","url_pdf":"http://openaccess.thecvf.com/content_ICCV_2019/papers/He_Mop_Moire_Patterns_Using_MopNet_ICCV_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":"mop-moire-patterns-using-mopnet","repo_url":"https://github.com/PKU-IMRE/MopNet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"image-enhancement","task_name":"Image Enhancement"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-enhancement-on-tip-2018","task":"Image Enhancement","dataset":"TIP 2018","model":"MopNet","rank_in_archive_order":5,"of":6,"metrics":{"PSNR":"27.75","SSIM":"0.895"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}