Abstract
AbstractFor decades, image enhancement has been considered one of the most important aspects in computer science because of its influence on a number of fields including but not limited to medical, security, banking and financial sectors. In this paper, a new gray level image (edge preserving) enhancement method called the harmony search algorithm (HSA) is proposed. HSA is a recently introduced population-based algorithm stemmed by the musical improvisation process when a group of musicians play the pitches of their instruments seeking for pleasing harmony. Tremendous successful stories of HSA application to a wide variety of optimization problems have been passed on at a large scale. In order to evaluate the proposed HSA-based image enhancement method, 14 standard images from the literature are used. For comparative evaluation, the results of the 14 enhanced image produced by HSA are compared with two classical image enhancement methods (i.e., Histogram Equalization algorithm and Image Adjacent algorithm...
Highlights
Image enhancement is a special procedure of processing images to produce output image that is more suitable for special applications such as contrast image enhancement, edge enhancement, removing noise, brightening an image and saturation transformations 16
The main purpose of image enhancement is to improve the quality of the images to be more visible for viewers or to extract their important features to provide better input for other appli
The parameters of the harmony search algorithm (HSA) needed for any optimization problem are determined in this step: the Harmony Memory Consideration Rate (HMCR) which determines the rate of selecting the value from the memory; the Harmony Memory Size (HMS) is similar to the population size in evolutionary algorithm (EA), Pitch Adjustment Rate (PAR) determines the probability of local improvement; the fret width (FW), determines the distance of adjustment, and Number of Improvisations (NI) or number of iterations
Summary
Image enhancement is a special procedure of processing images to produce output image that is more suitable for special applications such as contrast image enhancement, edge enhancement, removing noise, brightening an image and saturation transformations 16. The authors compared the proposed method with genetic algorithm where four images: Cameraman, Tyre, Pout and House with different sizes are utilized during the evaluation process They argued that the fitness values obtained and the number of edges produced by PSO are better than those produced by GA and original image. The purpose of these parameters is to find the best set values to give the highest fitness The authors compared their results with three existing image enhancement methods (linear contrast stretching, histogram equalization and genetic algorithm) using four gray-scale images Lady and Couple of size (256 × 256), Hut of size (250 × 150), and Duck of size (576 × 768).
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