An edge-preserving image denoising method with edge detection and probability modelling

Most classical denoising methods based on wavelet transform will make the edge of an image fuzzy, thus cause the decline of overall effect of image denoising inevitable. Due to this problem, we propose an edge-preserving denoising method with edge detection and probability modelling in this paper. This method applies dual-tree complex wavelet transform to an image and detects the edge of the image based on the wavelet coefficients, thus divides the wavelet coefficients into two parts: the edge part and the non-edge part. For each part, the wavelet coefficients are modelled as a generalized Laplacian distribution, but they are shrinked differently. For the edge part, we preserve more signal information and keep the edge of the image obvious; for the non-edge part, we shrink the wavelet coefficients more sharply to flat the image. Our experimental results, by comparing with several advanced image denoising algorithms, demonstrate that our method can yield better PSNR as well as preserve the edge of the image well.