{"title":"使用GMM和PLS回归的超分辨率","authors":"Y. Ogawa, Takahiro Hori, T. Takiguchi, Y. Ariki","doi":"10.1109/ISM.2012.62","DOIUrl":null,"url":null,"abstract":"In recent years, super-resolution techniques in the field of computer vision have been studied in earnest owing to the potential applicability of such technology in a variety of fields. In this paper, we propose a single-image, super-resolution approach using a Gaussian Mixture Model (GMM) and Partial Least Squares (PLS) regression. A GMM-based super-resolution technique is shown to be more efficient than previously known techniques, such as sparse-coding-based techniques. But the GMM-based conversion may result in over fitting. In this paper, an effective technique for preventing over fitting, which combines PLS regression with a GMM, is proposed. The conversion function is constructed using the input image and its self-reduction image. The high-resolution image is obtained by applying the conversion function to the enlarged input image without any outside database. We confirmed the effectiveness of this proposed method through our experiments.","PeriodicalId":282528,"journal":{"name":"2012 IEEE International Symposium on Multimedia","volume":"85 5-6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Super-resolution Using GMM and PLS Regression\",\"authors\":\"Y. Ogawa, Takahiro Hori, T. Takiguchi, Y. Ariki\",\"doi\":\"10.1109/ISM.2012.62\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, super-resolution techniques in the field of computer vision have been studied in earnest owing to the potential applicability of such technology in a variety of fields. In this paper, we propose a single-image, super-resolution approach using a Gaussian Mixture Model (GMM) and Partial Least Squares (PLS) regression. A GMM-based super-resolution technique is shown to be more efficient than previously known techniques, such as sparse-coding-based techniques. But the GMM-based conversion may result in over fitting. In this paper, an effective technique for preventing over fitting, which combines PLS regression with a GMM, is proposed. The conversion function is constructed using the input image and its self-reduction image. The high-resolution image is obtained by applying the conversion function to the enlarged input image without any outside database. We confirmed the effectiveness of this proposed method through our experiments.\",\"PeriodicalId\":282528,\"journal\":{\"name\":\"2012 IEEE International Symposium on Multimedia\",\"volume\":\"85 5-6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Symposium on Multimedia\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISM.2012.62\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Symposium on Multimedia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISM.2012.62","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In recent years, super-resolution techniques in the field of computer vision have been studied in earnest owing to the potential applicability of such technology in a variety of fields. In this paper, we propose a single-image, super-resolution approach using a Gaussian Mixture Model (GMM) and Partial Least Squares (PLS) regression. A GMM-based super-resolution technique is shown to be more efficient than previously known techniques, such as sparse-coding-based techniques. But the GMM-based conversion may result in over fitting. In this paper, an effective technique for preventing over fitting, which combines PLS regression with a GMM, is proposed. The conversion function is constructed using the input image and its self-reduction image. The high-resolution image is obtained by applying the conversion function to the enlarged input image without any outside database. We confirmed the effectiveness of this proposed method through our experiments.