In each step of anisotropic diffusion smoothing, noises must be managed to get better results. The mostly used method is
Gaussian filtering. However, the standard deviation of the Gaussian filter can't be accurately obtained and it should
change during the iterative process. Another problem is how to select a proper standard deviation to reducing noises
while preserving edges. Actually, facet model fitting can be taken as a natural way to overcome the drawbacks
mentioned above. Facet model fitting has the low-pass filtering performance adaptive to the image during evolution of
diffusion; it can also achieve balanced results for noise reduction and edge preserving. Experiments show the method can
preserve more edges as well as obtain higher peak signal-to-noise ratio as compared to other anisotropic diffusion based
selective smoothing approaches.