A novel median-contourlet for image denoising application

In this paper, a novel contourlet transform based on median filter is proposed. By using a novel median pyramidal decomposition, the noise distributing is analyzed for the image distorted by salt-and-pepper noise and Gaussian noise respectively. Comparing the Probability-Density -Functions of the detail coefficients of the each corresponding layer, it is found that these two kinds of noise mainly concentrate on the bottom high frequency layer. So a majority of noises can be removed by denoting zero the bottom layer coefficient. Median-Contourlet transform is completed when the second layer and other high frequency image is calculated by PDFB(Pyramidal Directional Filter Bank). After analysing of Contourlet coefficients, we select the best threshold to remove further the noises. Applying the same denoising method to images, the Median-Contourlet achieves obvious improvement in both subjective visual effect and SNR comparing with traditional contourlet transform.