The Contourlet Transform for Image De-noising Using Cycle Spinning

We propose a new method for image de-noising based on the contourlet transform, which has been recently introduced. Image de-noising by means of the contourlet transform introduces many visual artifacts due to the Gibbs-like phenomena. Due to the lack of translation invariance of the contourlet transform, we employ a cycle-spinning-based technique to develop translation invariant contourlet de-noising scheme. This scheme achieves enhanced estimation results for images that are corrupted with additive Gaussian noise over a wide range of noise variance. Our experiments show that the proposed approach outperforms the translation invariant wavelets both visually and in terms of the PSNR values, especially for the images that include mostly fine textures and contours.