标题:Accurate Prior Modeling in the Locally Adaptive Window-Based Wavelet Denoising
作者:Liu, Yun-Xia; Yang, Yang; Law, Ngai-Fong
通讯作者:Liu, YunXia
作者机构:[Liu, Yun-Xia] Univ Jinan, Sch Informat Sci & Engn, Jinan, Peoples R China.; [Liu, Yun-Xia] Shandong Prov Key Lab Network Based Intelligent C, Jinan 更多
会议名称:12th International Conference on Intelligent Computing (ICIC)
会议日期:AUG 02-05, 2016
来源:INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2016, PT II
出版年:2016
卷:9772
页码:523-533
DOI:10.1007/978-3-319-42294-7_47
关键词:Image denoising; Orthogonal wavelet transform; Adaptive parameter; estimation; Maximum likelihood estimation; Visual quality
摘要:The locally adaptive window-based (LAW) denoising method has been extensively studied in literature for its simplicity and effectiveness. However, our statistical analysis performed on its prior estimation reveals that the prior is not estimated properly. In this paper, a novel maximum likelihood prior modeling method is proposed for better characterization of the local variance distribution. Goodness of fit results shows that our proposed prior estimation method can improve the model accuracy. A modified LAW denoising algorithm is then proposed based on the new prior. Image denoising experimental results demonstrate that the proposed method can significantly improve the performance in terms of both peak signal-to noise ratio (PSNR) and visual quality, while maintain a low computation.
收录类别:CPCI-S;EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978818579&doi=10.1007%2f978-3-319-42294-7_47&partnerID=40&md5=50513a5f0ccaf5dde1b22bc436abf3a4
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