Walid Imoudene, L. Boubchir, Z. Messali, Messaouda Larbi
{"title":"Performance Evaluation of Iterative Denoising Algorithm Based on Variance Stabilizing Transform and Wavelet Thresholding","authors":"Walid Imoudene, L. Boubchir, Z. Messali, Messaouda Larbi","doi":"10.1109/ICAEE47123.2019.9014740","DOIUrl":null,"url":null,"abstract":"The restoration of images degraded by noise is one of the most important tasks in image processing. This paper deals with the recovery of an image from a Poisson noisy observations. More precisely, we have combined an iterative denoising algorithm based on Variance Stabilizing Transform (VST) with the conventional Wavelet Thresholding technique. At each iteration, a combination of the Poisson observations with the denoised estimate from the previous iteration is treated as scaled Poisson data and riltered through a VST scheme and wavelet thresholding. Experimental results show the effectiveness of the proposed method for denoising images corrupted by Poisson noise. Performance assessment is provided.","PeriodicalId":197612,"journal":{"name":"2019 International Conference on Advanced Electrical Engineering (ICAEE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Advanced Electrical Engineering (ICAEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAEE47123.2019.9014740","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The restoration of images degraded by noise is one of the most important tasks in image processing. This paper deals with the recovery of an image from a Poisson noisy observations. More precisely, we have combined an iterative denoising algorithm based on Variance Stabilizing Transform (VST) with the conventional Wavelet Thresholding technique. At each iteration, a combination of the Poisson observations with the denoised estimate from the previous iteration is treated as scaled Poisson data and riltered through a VST scheme and wavelet thresholding. Experimental results show the effectiveness of the proposed method for denoising images corrupted by Poisson noise. Performance assessment is provided.