由于提高Contourlet变换冗余性可以抑制去噪结果中的伪Gibbs现象，因此为了提高变换冗余度和避免数据量过大，以进行快速有效的图像去噪，提出了一种基于非抽样LP的Contourlet变换图像去噪方法。该方法首先对带噪图像进行非抽样LP多尺度分解；然后对各子带图像进行临界抽样的DFB分解，再采用尺度相关的分层模型对各子带图像进行阈值处理；最后对处理后的子带图像进行DFB和LP重建，以得到去噪后的图像。与同类型有关方法进行的对比实验表明，在去噪后图像的PSNR值上，该方法比常规Contourlet变换方法至少提高1dB；在完成时间方面，该方法比其他改进方法快1倍以上。

By enriching redundancy of the contourlet transform, it is possible to weaken pseudo-Gibbs phenomena in the process of image de-noising by thresholding. In order to remove noise from image effectively and quickly, by enriching redundancy of the contourlet transform and avoiding too much data, a method for image de-noising based on non-subsampled pyramid contourlet transform is proposed. The method decomposes noisy image using nonsubsampled LP for multi-scale, and decomposes sub-image using critical sampled DFB, then performs scale related threshold for shrinkage, finally reconstructs de-noised image. Experiments compared with other related methods show that the proposed method, on the PSNR values of the de-noised images, yields improvements up to 1dB over original contourlet transform; on the time consumption, costs half less than other improved methods.