摘要： 提出了一种基于贝叶斯估计的Contourlet域图像降噪方法。该方法对输入的带噪图像进行多尺度、多方向的Contourlet稀疏分解，并在Contourlet域利用最小Bayesian风险函数对分解系数进行估计，通过Contourlet反变换得到预降噪图像。实验结果表明，该方法较其他的Contourlet域收缩阈值降噪效果好，进一步提高了PSNR值和降低了MSE值，能获得更好的图像恢复的质量。

关键词: Contourlet变换, 稀疏表示, 收缩阈值, Bayesian估计

Abstract: This paper proposes a Contourlet domain image denoising method based on Bayesian estimation. By using Contourlet transform, the noised image is firstly decomposed into a low frequency subband and a set of multisacle and multidirectional high frequency subbands. The high frequency coefficients of the original image are estimated by the minimizing Bayesian risk. The denoising image is gotten by performing the inverse Contourlet transform to these estimated coefficients. Experimental results show that the denoising effect is better than that of other methods based on Contourlet transform.

Key words: Contourlet transform, sparse representation, threshold denoising, Bayesian estimation

中图分类号: 

• TP391