A second-order nonlinear anisotropic diffusion-based model for Gaussian additive noise removal is proposed. The method is based on a properly constructed edgestopping function and provides an efficient detail-preserving denoising. It removes additive noise, overcomes blurring effect, reduces the image staircasing and does not generate multiplicative noise, thus preserving boundaries and all the essential image features
very well. The corresponding PDE model is solved by a robust finite-difference based
iterative scheme consistent with the diffusion model. The method converges very fast
to the model solution, the existence and regularity of which is rigorously proved.