A spatial adaptive filter for smoothing of non-Gaussian texture noise

This paper contributes a novel technique for reducing the interference of non-Gaussian texture noise from images. Since the inherent properties of texture noise are very different from those of Gaussian white noise, the basic assumption of conventional image denoising techniques is invalid. Here we present a spatial adaptive filtering scheme to remove non-Gaussian texture noise from textile images based on local and non-local similarities. In order to exploit the high correlations among pixels, pixels with uniform texture local regions are estimated differently from those pixels located near edges, that is, for points located in local uniform texture regions, Gaussian weighted averaging of their neighbors can achieve the adaptive effect of the human visual system, whereas for edge points, to find pixels with similar local statistics both in the vicinity and far away can produce a sufficient set of pixels for reasonable averaging. This filtering strategy is applied to textile images corrupted by texture noise and the performance is demonstrated to outperform current state-of-art image denoising techniques.