A nonlinear adaptive regression process for noise corrupt images

Most existing nonlinear regression filtering techniques for image denoising are claimed to be edge preserving without considering the pixel position information. This will cause speckling effects on the denoised image and inconsistent smoothing in the vicinity of texture-rich areas. This paper proposes a novel denoising method to address this problem. The proposed method removes the low to intermediate noise using edge-preserving range filtering, thereby removing short, false edges. The updated edge map is used for subsequent filtering in which pixel intensities are smoothed according to their minimum distance to the closest edge point. This procedure is carried out in an iterative scheme until the edge map stabilizes. We compare existing denoising algorithms with the proposed method. Experimental results validate the effectiveness and efficiency of the proposed method.