利用方向特性实现非下采样Contourlet变换阈值去噪

系数阈值是流行的去噪方法，其中阈值方式与大小的选择是一个重要的技术问题．依据非下采样Contourlet分解系数尺度内与尺度间的相关性，考虑到相同尺度内不同方向上系数分布的聚集性依赖图像自身发生变化，提出一种利用方向特性实现非下采样Contourlet变换阈值去噪策略．对于被加性高斯白噪声污染的图像，实验中将利用方向特性实现非下采样Contourlet变换阈值去噪策略方法与小波阈值去噪、Contourlet变换去噪方法和非下采样Contourlet变换去噪方法进行了比较，结果表明利用方向特性实现非下采样Contourlet变换阈值去噪策略的峰值信噪比结果相比这些方法平均高出0.5~3.3dB，在边缘特征方面保持了良好的视觉效果．

Image denoising using the directional property in the NSCT domain

As the main prevailing denoising method, how the threshold function works and what the threshold value is are of the greatest importance. According to the interscale and intrascale dependencies of the coefficients in the non-subsampled Contourlet transform domain, and considering the change of coefficient’s aggregation with different directional subbands in the same scale, a novel non-subsampled Contourlet transform denoising scheme using the directional property (AD_NSCT) is proposed. This scheme can lead to enhanced estimation results for images that are corrupted with additive Gaussian noise over a wide range of noise variance. To evaluate the performance of the proposed algorithms, simulation results are compared with those by the algorithms, such as wavelet threshold, Contourlet transform threshold and non-subsampled Contourlet transform threshold for image denoising. The simulation results indicate that the proposed method outperforms the others 0.5~3.3dB in the PSNR, and keep a better visual result in edges information reservation as well.