A Novel Adaptive Shrinkage Threshold on Shearlet Transform for Image Denoising

被引：0
|
作者：
Islam, Sheikh Md Rabiul [1 ]
Huang, Xu [1 ]
Le, Kim [1 ]
机构：
[1] Univ Canberra, Fac Educ Sci Technol & Math, Canberra, ACT 2601, Australia
来源：
NEURAL INFORMATION PROCESSING, ICONIP 2014, PT III | 2014年 / 8836卷
关键词：
UIQI; Denoising; Kurtosis; Skewness; Shearlet transform; Adaptive threshold; WAVELET SHRINKAGE;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Shearlet is a new multidimensional and multiscale transform which is optimally efficient in representing image containing edges. In this paper an adaptive shrinkage threshold for image de-noising in shearlet domain is proposed. Experimental results show that images de-noised with the proposed approach had higher qualities than those produced with some of the other de-noising methods like wavelet-based, bandlet-based, shearlet-based and curvelet-based.
引用
收藏
页码：127 / 134
页数：8
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