—In all methods of image denoising, there is a problem always exists that is how to distinguish noise and edge. Now wavelet and contourlet are main tools in image denoising, but ...
Many image denoising methods can be characterized as minimizing "loss + penalty," where the "loss" measures the fidelity of the denoised image to the data, and ...
This paper presents an image denoising algorithm that uses principal component analysis (PCA) in conjunction with the non-local means image denoising. Image neighborhood vectors u...
Using statistical modeling in the wavelet domain, we address the problem of image denoising. Despite being effective, the denoised images can suffer from the Gibbs-like artifacts,...
In this paper, a Bayesian wavelet denoising procedure for multicomponent images is proposed. The procedure makes use of a noise-free single component image as prior information. T...