Image Denoising with Wavelet Markov Fields of Experts

 Abstract—Image denoising methods based on Markov random field (MRF) are often shown over-smooth phenomenon for strong noise image. Wavelet analysis has good time-frequency local ability and preserves the image edge information well for image denoising problem. Based on wavelet analysis and MRF theory, we propose a wavelet markov field of experts (WMFoE) framework to deal with image denoising problems. The noise image is divided into low-frequency and high-frequency component, and MRF are used to deal with low-frequency component. For high-frequency component, a clustering based soft-threshold method is used to remove the noise signal. Then, the restored image can be gotten by reconstruction from different components. Experiment results show that our method not only gets good PSNR and SSIM values but also preserves image edge information especially for strong noise image, compared with BM3D etc. state-of-art methods. 