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

Abstract

In the paper, a new approach to the impulsive noise removal in color images is presented. The new filtering design is based on the peer group concept, which determines the membership of a central pixel of the filtering window to its local neighborhood, in terms of the number of close pixels. Two pixels are declared as close if their distance in a given color space does not exceed a predefined threshold value. A pixel is treated as not corrupted by the impulsive noise process, if its peer group consists of at least two close pixels, otherwise this pixel is replaced by a weighted average of uncorrupted samples from the local neighborhood. The peer group size assigned to each pixel is used for the averaging operation, so that pixels which have many peers are taken with higher weight. The new filtering design proved to restore efficiently color images corrupted by even strong impulsive noise, while preserving tiny image details. The beneficial property of the proposed filter is its very low computational complexity, which allows its application in real-time image processing tasks.

Fast Technique of Impulsive Noise Suppression in Color Images

“…The calculation of weights, needed for the determination of the final output, do not significantly increase the computational burden. Furthermore, the new filter performance is comparable with the newly introduced FAPG (7) , which belongs to the most efficient denoising frameworks.…”

“…Therefore, the switching filters, which replace only the pixels detected as corrupted by the impulsive noise, offer much better denoising quality (6,7) . The efficiency of the switching filters is strongly dependent on the accuracy of the impulse detection scheme and the proper adjustment of the threshold, which discriminates between the impulses and uncorrupted pixels.…”

Decision-Based Marginal Total Variation Diffusion for Impulsive Noise Removal in Color Images

“…Malinski and Smolka [10] use adaptive switching technique. Particularly, after the concept of a peer group is introduced, many schemes based on it have been presented; for example, Morillas et al [11] use the fuzzy peer groups, and Malinski and Smolka [12] utilize the fast averaging peer group.…”

Directional 3D Peer-Group Filter for Color Image with Random Impulse Noise

“…Unfortunately, the efficiency of this method degrades rapidly with an increase in noise, such that few uncorrupted pixels remain within the window. Recent peer-group filters, such as the fast peer group filter for VMF (FPGFvmf) [7] and the fast averaging peer group filter (FAPGF) [8], have been extended to deal with color images; however, situations involving strong noise have seldom been investigated.…”

“…The performance of the proposed algorithm was also compared with other impulse noise filters: classical vector filters such as vector median filter (VMF) [1], basic vector directional filter (BVDF) [2], and directional-distance filter (DDF) [3], and other well-known vector filters such as adaptive vector median filter (AVMF) [4], robust switching VMF (RSVMF) [5], edge detection based switching VMF (EDSVMF) [6], fast peer group filter for VMF (FPGFvmf) [7], fast averaging peer group filter (FAPGF) [8], quaternion switching VMF(QSVMF) [9], quaternion directional switching VMF (QDSVMF) [10], and two-stage quaternion switching vector filter (TQSVF) [11]. Table I lists result of the performance comparison in terms of PSNR, MAE, and NCD.…”