{"title":"用于恢复被表面椒盐噪声破坏的图像的非凸模型","authors":"Yuan Liu, Peiqi Yu, Chao Zeng","doi":"arxiv-2409.11139","DOIUrl":null,"url":null,"abstract":"Image processing on surfaces has drawn significant interest in recent years,\nparticularly in the context of denoising. Salt-and-pepper noise is a special\ntype of noise which randomly sets a portion of the image pixels to the minimum\nor maximum intensity while keeping the others unaffected. In this paper, We\npropose the L$_p$TV models on triangle meshes to recover images corrupted by\nsalt-and-pepper noise on surfaces. We establish a lower bound for data fitting\nterm of the recovered image. Motivated by the lower bound property, we propose\nthe corresponding algorithm based on the proximal linearization method with the\nsupport shrinking strategy. The global convergence of the proposed algorithm is\ndemonstrated. Numerical examples are given to show good performance of the\nalgorithm.","PeriodicalId":501162,"journal":{"name":"arXiv - MATH - Numerical Analysis","volume":"29 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Nonconvex models for recovering images corrupted by salt-and-pepper noise on surfaces\",\"authors\":\"Yuan Liu, Peiqi Yu, Chao Zeng\",\"doi\":\"arxiv-2409.11139\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Image processing on surfaces has drawn significant interest in recent years,\\nparticularly in the context of denoising. Salt-and-pepper noise is a special\\ntype of noise which randomly sets a portion of the image pixels to the minimum\\nor maximum intensity while keeping the others unaffected. In this paper, We\\npropose the L$_p$TV models on triangle meshes to recover images corrupted by\\nsalt-and-pepper noise on surfaces. We establish a lower bound for data fitting\\nterm of the recovered image. Motivated by the lower bound property, we propose\\nthe corresponding algorithm based on the proximal linearization method with the\\nsupport shrinking strategy. The global convergence of the proposed algorithm is\\ndemonstrated. Numerical examples are given to show good performance of the\\nalgorithm.\",\"PeriodicalId\":501162,\"journal\":{\"name\":\"arXiv - MATH - Numerical Analysis\",\"volume\":\"29 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - MATH - Numerical Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.11139\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - MATH - Numerical Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.11139","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Nonconvex models for recovering images corrupted by salt-and-pepper noise on surfaces
Image processing on surfaces has drawn significant interest in recent years,
particularly in the context of denoising. Salt-and-pepper noise is a special
type of noise which randomly sets a portion of the image pixels to the minimum
or maximum intensity while keeping the others unaffected. In this paper, We
propose the L$_p$TV models on triangle meshes to recover images corrupted by
salt-and-pepper noise on surfaces. We establish a lower bound for data fitting
term of the recovered image. Motivated by the lower bound property, we propose
the corresponding algorithm based on the proximal linearization method with the
support shrinking strategy. The global convergence of the proposed algorithm is
demonstrated. Numerical examples are given to show good performance of the
algorithm.