Image Denoising Based on Edge Recovery

Common smoothing algorithms can be considered as different kinds of diffusion, which try to make a trade off between denoising and edge information reservation. In this paper, however, we deal with the denoising problem from a novel thought—employing an inverse diffusion to recover edge information after image smoothing, combining adaptive scale filtering and inverse diffusion function for image denoising. This method adopts Minimal Description Length (MDL) rule to adaptively choose the optimal scale for each pixel to smooth the image and improves inverse diffusion function to recover the edges in the degraded image. Experiments show that this method is more effective than classical filters and anisotropic diffusion.