On the application of the reachability distance in the suppression of mixed Gaussian and impulsive noise in color images

In this paper, we address the problem of mixed Gaussian and impulsive noise reduction in color images. A robust filtering technique is proposed, which is utilizing a novel concept of pixels dissimilarity based on the reachability distance. The structure of the denoising method requires the estimation of the impulsiveness of each pixel in the processing block using the introduced local reachability concept. Furthermore, we determine the similarity of each pixel in the block to the central patch consisting of the processed pixel and its neighbors. Both measures are calculated as an average of modified reachability distances to the most similar pixels of the central patch and the final filtering output is a weighted average of all pixels belonging to the processing block. The proposed technique was compared with widely used filtering methods and the performed experiments proved its satisfying denoising properties. The introduced filtering design is insensitive to outliers and their clusters introduced by the impulsive noise process, preserves details and is able to efficiently suppress the Gaussian noise while enhancing the image edges. Additionally, we proposed a method which estimates the noise contamination intensity, so that the proposed filter is able to adaptively tune its parameters.

TV-based Impulsive Noise Reduction with Weber Law Detector

This article proposes a new technique for impulsive noise removal. This technique consists of two steps: (1) impulsive noise detection, and (2) impulsive noise suppression. The proposed method exploits the effectiveness of Weber Law in detecting and locating the impulsive noise appearing in the corrupted image. The occurrence of impulsive noise is then reduced and suppressed using the Total Variation-based approach with the detected noise map obtained from the Weber Law detector. As documented in the Experimental section, the proposed method offers promising results in terms of visual investigation. In addition, it also gives superior results compared to that of the former competing schemes under objective assessment. Thus, it can be regarded as a good candidate for impulsive noise removal algorithm.