{"title":"基于实例的单色图像色调映射算法","authors":"Chunzhi Gu, Chao Zhang","doi":"10.1109/NicoInt50878.2020.00033","DOIUrl":null,"url":null,"abstract":"We propose an example-based tone mapping algorithm for monochrome image. Specifically, the distribution of pixel intensity of the example image is modeled with Gaussian mixture model (GMM) whose centroids are initialized by the source image. With GMM centriods iteratively shifted by expectation-maximization (EM) optimization, tone of the source image gradually approximates the example image. Experiment with qualitative results validate the effectiveness of our method.","PeriodicalId":230190,"journal":{"name":"2020 Nicograph International (NicoInt)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Example-based Tone Mapping Algorithm for Monochrome Image\",\"authors\":\"Chunzhi Gu, Chao Zhang\",\"doi\":\"10.1109/NicoInt50878.2020.00033\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose an example-based tone mapping algorithm for monochrome image. Specifically, the distribution of pixel intensity of the example image is modeled with Gaussian mixture model (GMM) whose centroids are initialized by the source image. With GMM centriods iteratively shifted by expectation-maximization (EM) optimization, tone of the source image gradually approximates the example image. Experiment with qualitative results validate the effectiveness of our method.\",\"PeriodicalId\":230190,\"journal\":{\"name\":\"2020 Nicograph International (NicoInt)\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 Nicograph International (NicoInt)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NicoInt50878.2020.00033\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Nicograph International (NicoInt)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NicoInt50878.2020.00033","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Example-based Tone Mapping Algorithm for Monochrome Image
We propose an example-based tone mapping algorithm for monochrome image. Specifically, the distribution of pixel intensity of the example image is modeled with Gaussian mixture model (GMM) whose centroids are initialized by the source image. With GMM centriods iteratively shifted by expectation-maximization (EM) optimization, tone of the source image gradually approximates the example image. Experiment with qualitative results validate the effectiveness of our method.
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