Moth Swarm Algorithm for Image Contrast Enhancement

• Published in: Knowledge-based systems 2021-01, Vol.212, p.106607, Article 106607
• Main Authors: Luque-Chang, Alberto, Cuevas, Erik, Pérez-Cisneros, Marco, Fausto, Fernando, Valdivia-González, Arturo, Sarkar, Ram
• Format: Article
• Language: English
• Subjects: Algorithms; Color imagery; Computer vision; Entropy; Global optimization; Histograms; Image contrast; Image enhancement; Image processing; Moth Swarm Algorithm; Pixels; Visual perception; Visualization
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2020.106607

Image Contrast Enhancement (ICE) is a crucial step in several image processing and computer vision applications. Its main objective is to improve the quality of the visual information contained in the processed images. The presence of noise and small sets of pixels in images are not only irrelevant for their visualization. It also negatively affects the improvement process of ICE schemes since the inclusion of irrelevant information avoids the appropriate distribution of significant pixel intensities in the enhanced image. As a consequence of this effect, most of the proposed ICE methods present different associated problems such as the production of undesirable artifacts, noise amplification, over saturation and bad human visual perception. In this paper, an Image Contrast Enhancement (ICE) method for grayscale and color images is presented. The proposed approach has the propriety of eliminating noisy and irrelevant information in order to improve the distribution capacity of significant pixel intensities in the enhanced image. Our method eliminates multiple groups of a very small number of pixels that, according to their characteristics, do not represents any object or important detail of the image. This process is done by the Mean-shift algorithm, which is used to replace such sets of irrelevant pixels in the original histogram by significant pixel densities represented by local maxima. Then, the Moth Swarm Algorithm (MSA) is used to redistribute the pixel intensities of the reduced histogram so that the value from Kullback–Leibler entropy (KL-entropy) has been maximized. The proposed approach has been tested considering different public datasets commonly used in the literature. Its results are also compared with those produced by other well-known ICE techniques. Evaluation of the experimental results demonstrates that the proposed approach highlights the important details of the image also improving its human visual appearance. •It uses an elimination process to reduce irrelevant features.•It uses the KL-entropy for the enhancement process.•It considers the use of the MSA to find the best enhanced image.•Its results are competitive in comparison with other state-of-the art methods.