Image Contrast Improvement using the Intelligent Optimization - Artificial Whale Algorithm

One of the preprocessing stages of image processing is image enhancement. In order to make an image useful for analytics and the human visual system, image enhancement can successfully improve the image's look for realization purposes. The primary goal of image enhancement is to bring out hidden detail or increase contrast in low-contrast images. The goal of contrast enhancement is to improve an image's quality so that it's better suited for a certain presentation. Numerous image enhancement techniques have been proposed up to this point for various presentations, and a great deal of effort has been invested into figuring out how to further improve the quality of the enhanced outputs while using less memory and computing power. This paper approaches image contrast enhancement as an optimization problem, using a parameterized transformation function that incorporates both local and global image information to boost contrast. To determine the ideal number of parameters while maximizing the objective function, an intelligent optimization-artificial approach called the Whale optimization algorithm (WOA) was applied. The objective function consisted of the image's edge information and entropy. The results of the proposed method were compared with PSO and two traditional methods of enhancement, namely contrast stretching and histogram equalization. The results of the experiment show that the recommended approach performs better than the others.

کلیدواژه ها:

Image enhancement ، Particle swarm optimization ، Whale optimization algorithm ، Histogram equalization ، Contrast stretching