A new framework for image impulse noise removal with postprocessing

Impulse noise is commonly encountered during image transmission and many methods have been proposed to remove it. Although it is now possible to recover the true image reasonably well, even under severe noise (90% pixel contamination), essentially all methods published so far follow the standard procedure of noisy pixel detection/classification and then noisy pixel value reconstruction, without any further processing. In this paper we show an interesting empirical discovery that the traditionally denoised image tends to have the estimation error with a Laplacian distribution, which makes it possible to add a postprocessing stage to denoise the traditionally obtained result with this new type of noise. We propose a practical algorithm within this new framework and experimental results show that superior results can be obtained over previously published methods.