Impulse-mowing anisotropic diffusion filter for image denoising

Image denoising is still a challenging problem, particularly when the noise is made combining Gaussian noise and random-valued impulses. This article is concerned with diffusion-based denoising methods which can suppress such complicated noises effectively, preserving fine structures. We introduce a novel impulse-mowing anisotropic diffusion (IMAD) filter to cut out impulses and local maxima/minima without affecting surrounding pixel values. It has been numerically verified that the suggested mean filter carries out both mowing impulses and restoring fine structures satisfactorily. It outperforms nonlinear median filters, measured in PSNR and visual inspection.