{"title":"基于迭代正则化的自适应阈值hosvd图像去噪算法","authors":"Rodion Movchan, Zhengwei Shen","doi":"10.1109/ICIP.2017.8296831","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a very simple 3D patch stack based image denoising method by Higher Order Singular Value Decomposition (HOSVD). We used the idea of iterative regularization from spatially adaptive iterative singular-value thresholding(SAIST) to design our algorithm, which indicates more faster convergence speed than some other methods. By using the parallel computing technique for implementing the algorithm, the computational complexity is highly reduced. The experiments also show good PNSR result with different noise levels.","PeriodicalId":229602,"journal":{"name":"2017 IEEE International Conference on Image Processing (ICIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Adaptive thresholding hosvd algorithm with iterative regularization for image denoising\",\"authors\":\"Rodion Movchan, Zhengwei Shen\",\"doi\":\"10.1109/ICIP.2017.8296831\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a very simple 3D patch stack based image denoising method by Higher Order Singular Value Decomposition (HOSVD). We used the idea of iterative regularization from spatially adaptive iterative singular-value thresholding(SAIST) to design our algorithm, which indicates more faster convergence speed than some other methods. By using the parallel computing technique for implementing the algorithm, the computational complexity is highly reduced. The experiments also show good PNSR result with different noise levels.\",\"PeriodicalId\":229602,\"journal\":{\"name\":\"2017 IEEE International Conference on Image Processing (ICIP)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE International Conference on Image Processing (ICIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIP.2017.8296831\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Image Processing (ICIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP.2017.8296831","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive thresholding hosvd algorithm with iterative regularization for image denoising
In this paper, we propose a very simple 3D patch stack based image denoising method by Higher Order Singular Value Decomposition (HOSVD). We used the idea of iterative regularization from spatially adaptive iterative singular-value thresholding(SAIST) to design our algorithm, which indicates more faster convergence speed than some other methods. By using the parallel computing technique for implementing the algorithm, the computational complexity is highly reduced. The experiments also show good PNSR result with different noise levels.