摘要
为更好地对图像进行稀疏表示,以改善去噪效果,提出一种傅里叶变换与非下采样轮廓波变换(NSCT)相结合的自适应阈值去噪算法。在傅里叶域中对含噪图像去噪,在NSCT域中利用分层噪声估计的贝叶斯阈值算法,结合多尺度多方向的能量阈值修正方案自适应地滤除剩余噪声。实验结果表明,该算法的去噪性能较好。
In order to express a sparse image better and achieve better de-noising effect, an adaptive threshold de-noising algorithm combined with Fourier Transform(FT) and Non-subsampled Contourlet Transform(NSCT) is proposed. The noisy image is de-noised in Fourier domain, and the remaining noise is filtered out in NSCT domain. It is based on stratified noise estimation and adaptive Bayes threshold, combined with a flexible multi-scale and multi-directional energy correction threshold scheme, Experimental results show that the proposed algorithm can improve de-noising performance.
出处
《计算机工程》
CAS
CSCD
2012年第17期238-241,共4页
Computer Engineering
基金
湖南省教育厅科研基金资助项目(10C1263)
湘潭大学科研基金资助项目(11QDZ11)
关键词
图像去噪
傅里叶变换
非下采样轮廓波变换
自适应阈值
贝叶斯框架
image de-noising
Fourier Transform(FT)
Non-subsampled Contourlet Transform(NSCT)
adaptive threshold
Bayes frame