CASIA OpenIR  > 智能制造技术与系统研究中心  > 多维数据分析
肖志云; 崔峰; 彭思龙; XiaoZhiyun; CuiFeng; PengSilong,
Source Publication计算机工程与应用,
Other AbstractBy using adaptive threshold classification, wavelet coefficiencts are classified into two categories: large(signification) coefficients and small (insignification) coefficients. The large coefficients are denoised by bivariate shrinkage model with interscale depency, and the small coefficients are denoised by zeros-mean Gaussian meodel with high local correlation. Cyclespinning technique is used to suppress the artifacts that may exisst in the denoised images. Experimental results show that the method imporves signifcantly in the PSNR and sujective visula effect of the denoised images.
Keyword图像降噪 / 小波变换 / 自适应阈值 / 双变量模型
Document Type期刊论文
Corresponding Author肖志云
Recommended Citation
GB/T 7714
肖志云,崔峰,彭思龙,等. 基于阈值分类的小波域混合模型图像降噪[J]. 计算机工程与应用,,2005,41(4)(2005年04期):19-22.
APA 肖志云,崔峰,彭思龙,XiaoZhiyun,CuiFeng,&PengSilong,.(2005).基于阈值分类的小波域混合模型图像降噪.计算机工程与应用,,41(4)(2005年04期),19-22.
MLA 肖志云,et al."基于阈值分类的小波域混合模型图像降噪".计算机工程与应用, 41(4).2005年04期(2005):19-22.
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