Article(id=1242116910456837017, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242116865502282190, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1230048000000, receivedDateStr=2008-12-24, revisedDate=-2209017600000, revisedDateStr=1900-01-01, acceptedDate=null, acceptedDateStr=null, onlineDate=1234454400000, onlineDateStr=2009-02-13, pubDate=1234454400000, pubDateStr=2009-02-13, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1234454400000, onlineIssueDateStr=2009-02-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774073348863, creator=sys-migrate, updateTime=1774073348863, updator=sys-migrate, issue=Issue{id=1242116865502282190, tenantId=1146029695717560320, journalId=1146031591421210625, year='2009', volume='27', issue='0903', pageStart='3', pageEnd='126', issueExtLink='null', onlineDate='null', pubDate='1234454400000', pubDateStr='2009-02-13', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774073338149, creator='sys-migrate', updateTime=1774073338149, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=31, endPage=34, ext={EN=ArticleExt(id=1242116913808081514, articleId=1242116910456837017, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Image Denoising Based on Gaussian Scale Mixtures in the Curvelet Domain, columnId=1242116080374710456, journalTitle=Science & Technology Review, columnName=Articles, runingTitle=null, highlight=null, articleAbstract=A new method using Gaussian Scales Mixtures in the Curvelet domain is proposed for image denoising. The Curvelet local coefficient model is first built using Gaussian Scale Mixtures. Then, the coefficients are estimated using Bayes least squares estimator. This algorithm combines the merits of Curvelet for image edge representation and Gaussian Scale Mixtures for capturing correlation of local coefficients. Some numerical experiments show the effectiveness of our method for image donoising, especially, for texture and detail predominated images., authors=null, authorsList=null, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=OfYnUCv4g3w90syXABH+iA==, pdfFileSize=480699, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1242116913325736552, articleId=1242116910456837017, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=基于高斯比例混合模型图像Curvelet域去噪, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=提出了一种基于高斯比例混合模型的图像Curvelet域去噪算法，改善了图像的去噪效果。首先对图像进行Curvelet变换，然后建立系数邻域的高斯比例混合模型，最后在模型基础上用贝叶斯最小二乘估计方法对系数进行估计。算法有效结合了Curvelet变换对图像边缘的高效表示能力和高斯比例混合模型对邻域系数相关性的概括能力。实验结果表明，在主观视觉上，该算法对图像边缘进行了很好的保护；在峰值信噪比上较其他算法也有所改善；特别是对纹理细节比较丰富的图像，去噪效果更加明显。, authors=黄华,王孝通, authorsList=黄华;王孝通, authorCompany=海军大连舰艇学院 航海系, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=G5n+2x2+JUTWBw9Km7nJ/w==, pdfFileSize=480699, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null)}, authors=null, keywords=[Keyword(id=1242116913015358052, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242116910456837017, language=CN, orderNo=1, keyword=图像去噪), 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