Xuan Wu, Songze Tang, Lili Huang, W. Shao, Pengfei Liu, Zhihui Wei
{"title":"通过向量hessian frobenius范数正则化实现鲁棒色彩去马赛克","authors":"Xuan Wu, Songze Tang, Lili Huang, W. Shao, Pengfei Liu, Zhihui Wei","doi":"10.1109/SIPROCESS.2016.7888244","DOIUrl":null,"url":null,"abstract":"Single sensor camera captures scenes using a color filter array, such that each pixel samples only one of the three primary colors. A process called color demosaicking (CDM) is used to produce full color image. In this paper, we present a new variational model for high quality CDM. The robust data term is measured by Z1-norm to repress the heavy tailed artifacts. The regularization term is measured by vectorial Hessian Frobenius norm (VHFN) to capture the higher order edges as well as the intra-correlations across different channels simultaneously. To solve the proposed model, an efficient algorithm is designed using alternating direction method of multiplier (ADMM). Experimental results demonstrate that the proposed CDM method outperforms many state-of-the-art methods in reducing color artifacts, preserving the sharp edges and reconstructing fine details.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Robust color demosaicking via vectorial hessian frobenius norm regularization\",\"authors\":\"Xuan Wu, Songze Tang, Lili Huang, W. Shao, Pengfei Liu, Zhihui Wei\",\"doi\":\"10.1109/SIPROCESS.2016.7888244\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Single sensor camera captures scenes using a color filter array, such that each pixel samples only one of the three primary colors. A process called color demosaicking (CDM) is used to produce full color image. In this paper, we present a new variational model for high quality CDM. The robust data term is measured by Z1-norm to repress the heavy tailed artifacts. The regularization term is measured by vectorial Hessian Frobenius norm (VHFN) to capture the higher order edges as well as the intra-correlations across different channels simultaneously. To solve the proposed model, an efficient algorithm is designed using alternating direction method of multiplier (ADMM). Experimental results demonstrate that the proposed CDM method outperforms many state-of-the-art methods in reducing color artifacts, preserving the sharp edges and reconstructing fine details.\",\"PeriodicalId\":142802,\"journal\":{\"name\":\"2016 IEEE International Conference on Signal and Image Processing (ICSIP)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE International Conference on Signal and Image Processing (ICSIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIPROCESS.2016.7888244\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIPROCESS.2016.7888244","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Robust color demosaicking via vectorial hessian frobenius norm regularization
Single sensor camera captures scenes using a color filter array, such that each pixel samples only one of the three primary colors. A process called color demosaicking (CDM) is used to produce full color image. In this paper, we present a new variational model for high quality CDM. The robust data term is measured by Z1-norm to repress the heavy tailed artifacts. The regularization term is measured by vectorial Hessian Frobenius norm (VHFN) to capture the higher order edges as well as the intra-correlations across different channels simultaneously. To solve the proposed model, an efficient algorithm is designed using alternating direction method of multiplier (ADMM). Experimental results demonstrate that the proposed CDM method outperforms many state-of-the-art methods in reducing color artifacts, preserving the sharp edges and reconstructing fine details.