Adaptive rank weighted switching filter for impulsive noise removal in color images

Abstract

In this paper, a novel approach to the problem of impulsive noise removal in color digital images is presented. The described switching filter is based on the rank weighted, cumulated pixel dissimilarity measures, which are used for the detection of image samples contaminated by impulsive noise process. The introduced adaptive design enables the filter to tune its parameters to the amount of impulsive noise corrupting the image. The comparison with existing denoising schemes shows that the new technique more efficiently removes the impulses introduced by the noise process, while better preserving image details. An important feature of the new filter is its low computational complexity, which allows for its application in real-time applications.

Fast averaging peer group filter for the impulsive noise removal in color images

“…1. The FPGF belongs to the fastest filters known from the literature and its efficiency is comparable for low noise contamination levels with the novel noise reduction method [22,27,34,38,60,76,87,88]. As the analyzed techniques belong to the class of switching filters [2,21,22,38], to exclude the effect of the image corruption intensity on the computational load, our analysis will focus on the number of elementary operations performed by impulse detection process and the number of elementary operations needed to perform the pixel replacement separately.…”

“…The VMF output is always a pixel from the filtering window, and when all pixels are corrupted by a noise process, the vector median output is also noisy. To circumvent this unwanted behavior, the pixels with the lowest ranks can be averaged, which leads to a better filtering performance [25,[28][29][30][31][32][33][34]. The dissimilarity of color pixels is usually defined in terms of the Euclidean distance in the RGB color space, however, other measures of vector dissimilarity, like the angular distance can be also applied [31,[35][36][37][38][39][40][41].…”

Binary image denoising using a quantum multilayer self organizing neural network

“…Among the filtering approaches, several efforts have also been invested. Smolka et al [12] proposed a novel approach to the problem of impulsive noise removal in color digital images. The proposed switching filter is based on the rank weighted, cumulated pixel dissimilarity measures, which are used for the detection of image samples contaminated by impulsive noise process.…”

Fast adaptive switching technique of impulsive noise removal in color images

“…However, when all pixels of W are affected, for example by additional Gaussian noise, the output is also noisy. Numerous solutions devoted to the elimination of this undesired behavior were introduced, resulting in significantly better filtering performance [12][13][14][15]. To increase the VMF efficiency, weights are assigned to the distances between pixels, which privilege the central pixel of the filtering window, thus diminishing the number of unnecessarily altered pixels [16,17].…”