Color image denoising using evolutionary computation

Abstract

Noise suppression in multichannel data sets, such as color images, has drawn much attention in the last few years. An issue of paramount importance in designing color image filters is the determination of the coefficients that should be used to weight the inputs to the filter. In this study, we propose an evolutionary computation-based approach to select and optimize the coefficients in the class of weighted vector directional filters. Using a genetic algorithm, we were able to adapt the filter weights to match varying image and noise characteristics. Extended experimentation with realistic image processing applications, including television image enhancement and virtual restoration of artworks, indicates that the proposed filters are capable of removing noise while preserving chromaticity information, edges, and fine details, as well as structural image content.

Edge-preserving color image denoising through tensor voting

“…However, its main drawback is that it is highly time consuming. A different spatial domain approach applies evolutionary computation [21]. However, its scope of use is limited, since it requires a training stage.…”

Fast detection and removal of impulsive noise using peer groups and fuzzy metrics

“…In [2,8,22,23,39], different techniques are presented to calculate coefficients used to determine the filter output as a combination of the vectors in the filtering window. Approaches which use some weights to perform a weighted vector median operation are proposed in [17,19,24,28]. In [1,4,20,26,27,38], the authors propose to determine first if the vector in consideration is likely to be noisy using cluster analysis, [1], a test respect to the mean of the vectors in the window, [20], a statistical test respect to the mean or median of the vectors, [4,26,27], and then apply the filtering operation only to those noisy pixels.…”

Some improvements for image filtering using peer group techniques

“…In order to overcome these drawbacks, a series of nonlinear filters have been proposed. These filters can be classified into the following categories: switching filters , filters using weighting coefficients [35][36][37][38][39][40][41], fuzzy filters [42][43][44][45][46][47][48][49], neuro-fuzzy filters [50][51][52], and partition-based filters [53][54][55].…”