{"title":"基于高强调滤波和相似函数的镜面分量分离的一般改进方法","authors":"Takahisa Yamamoto, A. Nakazawa","doi":"10.3169/MTA.7.92","DOIUrl":null,"url":null,"abstract":"Separating reflection components is a fundamental problem in computer vision and useful for many applications such as image quality. We propose a novel method that improves the accuracy of separating reflection components from a single image. Although several algorithms for separating reflection components have been proposed, our method can additionally improve the accuracy based on their results. First, we obtain diffuse and specular components by using an existing algorithm. Then, we apply a high-emphasis filter for each component. Since the responses of the high-emphasis filter where the separation fails become larger than the original values, we can detect erroneous pixels. Thus, we replace separation results of these erroneous pixels with results of other reference pixels from the image considering the similarity between the target and reference pixels. Experimental results show that our method can improve at most 13.61 dB in terms of the Peak Signal-to-Noise Ratio (PSNR).","PeriodicalId":41874,"journal":{"name":"ITE Transactions on Media Technology and Applications","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3169/MTA.7.92","citationCount":"25","resultStr":"{\"title\":\"[papers] General Improvement Method of Specular Component Separation Using High-Emphasis Filter and Similarity Function\",\"authors\":\"Takahisa Yamamoto, A. Nakazawa\",\"doi\":\"10.3169/MTA.7.92\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Separating reflection components is a fundamental problem in computer vision and useful for many applications such as image quality. We propose a novel method that improves the accuracy of separating reflection components from a single image. Although several algorithms for separating reflection components have been proposed, our method can additionally improve the accuracy based on their results. First, we obtain diffuse and specular components by using an existing algorithm. Then, we apply a high-emphasis filter for each component. Since the responses of the high-emphasis filter where the separation fails become larger than the original values, we can detect erroneous pixels. Thus, we replace separation results of these erroneous pixels with results of other reference pixels from the image considering the similarity between the target and reference pixels. Experimental results show that our method can improve at most 13.61 dB in terms of the Peak Signal-to-Noise Ratio (PSNR).\",\"PeriodicalId\":41874,\"journal\":{\"name\":\"ITE Transactions on Media Technology and Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2019-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.3169/MTA.7.92\",\"citationCount\":\"25\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ITE Transactions on Media Technology and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3169/MTA.7.92\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ITE Transactions on Media Technology and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3169/MTA.7.92","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
[papers] General Improvement Method of Specular Component Separation Using High-Emphasis Filter and Similarity Function
Separating reflection components is a fundamental problem in computer vision and useful for many applications such as image quality. We propose a novel method that improves the accuracy of separating reflection components from a single image. Although several algorithms for separating reflection components have been proposed, our method can additionally improve the accuracy based on their results. First, we obtain diffuse and specular components by using an existing algorithm. Then, we apply a high-emphasis filter for each component. Since the responses of the high-emphasis filter where the separation fails become larger than the original values, we can detect erroneous pixels. Thus, we replace separation results of these erroneous pixels with results of other reference pixels from the image considering the similarity between the target and reference pixels. Experimental results show that our method can improve at most 13.61 dB in terms of the Peak Signal-to-Noise Ratio (PSNR).