{"title":"去噪:成像、逆问题和机器学习的强大构建模块","authors":"Peyman Milanfar, Mauricio Delbracio","doi":"arxiv-2409.06219","DOIUrl":null,"url":null,"abstract":"Denoising, the process of reducing random fluctuations in a signal to\nemphasize essential patterns, has been a fundamental problem of interest since\nthe dawn of modern scientific inquiry. Recent denoising techniques,\nparticularly in imaging, have achieved remarkable success, nearing theoretical\nlimits by some measures. Yet, despite tens of thousands of research papers, the\nwide-ranging applications of denoising beyond noise removal have not been fully\nrecognized. This is partly due to the vast and diverse literature, making a\nclear overview challenging. This paper aims to address this gap. We present a comprehensive perspective\non denoisers, their structure, and desired properties. We emphasize the\nincreasing importance of denoising and showcase its evolution into an essential\nbuilding block for complex tasks in imaging, inverse problems, and machine\nlearning. Despite its long history, the community continues to uncover\nunexpected and groundbreaking uses for denoising, further solidifying its place\nas a cornerstone of scientific and engineering practice.","PeriodicalId":501289,"journal":{"name":"arXiv - EE - Image and Video Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning\",\"authors\":\"Peyman Milanfar, Mauricio Delbracio\",\"doi\":\"arxiv-2409.06219\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Denoising, the process of reducing random fluctuations in a signal to\\nemphasize essential patterns, has been a fundamental problem of interest since\\nthe dawn of modern scientific inquiry. Recent denoising techniques,\\nparticularly in imaging, have achieved remarkable success, nearing theoretical\\nlimits by some measures. Yet, despite tens of thousands of research papers, the\\nwide-ranging applications of denoising beyond noise removal have not been fully\\nrecognized. This is partly due to the vast and diverse literature, making a\\nclear overview challenging. This paper aims to address this gap. We present a comprehensive perspective\\non denoisers, their structure, and desired properties. We emphasize the\\nincreasing importance of denoising and showcase its evolution into an essential\\nbuilding block for complex tasks in imaging, inverse problems, and machine\\nlearning. Despite its long history, the community continues to uncover\\nunexpected and groundbreaking uses for denoising, further solidifying its place\\nas a cornerstone of scientific and engineering practice.\",\"PeriodicalId\":501289,\"journal\":{\"name\":\"arXiv - EE - Image and Video Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - EE - Image and Video Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.06219\",\"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 - EE - Image and Video Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.06219","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning
Denoising, the process of reducing random fluctuations in a signal to
emphasize essential patterns, has been a fundamental problem of interest since
the dawn of modern scientific inquiry. Recent denoising techniques,
particularly in imaging, have achieved remarkable success, nearing theoretical
limits by some measures. Yet, despite tens of thousands of research papers, the
wide-ranging applications of denoising beyond noise removal have not been fully
recognized. This is partly due to the vast and diverse literature, making a
clear overview challenging. This paper aims to address this gap. We present a comprehensive perspective
on denoisers, their structure, and desired properties. We emphasize the
increasing importance of denoising and showcase its evolution into an essential
building block for complex tasks in imaging, inverse problems, and machine
learning. Despite its long history, the community continues to uncover
unexpected and groundbreaking uses for denoising, further solidifying its place
as a cornerstone of scientific and engineering practice.