{"title":"利用紧框架特征增强焦点测量中的噪声鲁棒性","authors":"Yan-Ran Li;Junwei Liu;Zhangtao Ye;Lixin Shen;Xiaosheng Zhuang","doi":"10.1109/LSP.2025.3553425","DOIUrl":null,"url":null,"abstract":"Focus measures are widely used to assess image clarity in various fields, such as photography and computer vision. However, many existing focus measures face challenges in balancing noise robustness and measurement capability. In this letter, a novel focus measure called Variance of Tight Framelet Feature (VTFF) is proposed to address this challenge. VTFF leverages the advantages of tight framelet features and variance information in feature maps to provide a robust and accurate assessment of image focus. Experimental results on both synthetic and real-world data demonstrate its superior performance compared to recent focus measures in measurement capability, noise robustness, and real-time performance.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1435-1439"},"PeriodicalIF":3.2000,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing Noise Robustness in Focus Measure Using Tight Framelet Features\",\"authors\":\"Yan-Ran Li;Junwei Liu;Zhangtao Ye;Lixin Shen;Xiaosheng Zhuang\",\"doi\":\"10.1109/LSP.2025.3553425\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Focus measures are widely used to assess image clarity in various fields, such as photography and computer vision. However, many existing focus measures face challenges in balancing noise robustness and measurement capability. In this letter, a novel focus measure called Variance of Tight Framelet Feature (VTFF) is proposed to address this challenge. VTFF leverages the advantages of tight framelet features and variance information in feature maps to provide a robust and accurate assessment of image focus. Experimental results on both synthetic and real-world data demonstrate its superior performance compared to recent focus measures in measurement capability, noise robustness, and real-time performance.\",\"PeriodicalId\":13154,\"journal\":{\"name\":\"IEEE Signal Processing Letters\",\"volume\":\"32 \",\"pages\":\"1435-1439\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-03-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Signal Processing Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10935663/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Signal Processing Letters","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10935663/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Enhancing Noise Robustness in Focus Measure Using Tight Framelet Features
Focus measures are widely used to assess image clarity in various fields, such as photography and computer vision. However, many existing focus measures face challenges in balancing noise robustness and measurement capability. In this letter, a novel focus measure called Variance of Tight Framelet Feature (VTFF) is proposed to address this challenge. VTFF leverages the advantages of tight framelet features and variance information in feature maps to provide a robust and accurate assessment of image focus. Experimental results on both synthetic and real-world data demonstrate its superior performance compared to recent focus measures in measurement capability, noise robustness, and real-time performance.
期刊介绍:
The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.