Image Denoising Based On Total Variation Model In Wavelet Shrinkage

It is widely accepted that the wavelet transform (DWT) is a very attractive tool to deal with non-stationary signal due to the muti-resolution property. However, Gibbs phenomena and artifacts may be caused near the edges if the wavelet coefficients are modified by wavelet visual shrinking threshold. To overcome this problem, the wavelet based total variation regularized model is established according to the image restoration model of L.Rudin, S.Osher and E.Fatemi. In this method, wavelet coefficients to be filtered are selected by wavelet threshold technique. Then the image reconstructed by the nonzero wavelet coefficients is selective smoothing. We propose a complete numerical iterative scheme for this variational problem. Experiments show our algorithm is efficiency to improve the image's visual quality, and achieve a better compromise between noise suppressing and edge preserved, furthermore the reconstructing image has less oscillations near edges.