Grayscale Image Enhancement Using Water Cycle Algorithm

Recent developments in engineering and computer sciences have heightened the need for digital image enhancement. Most of previously reported works, however, focused on enhancement using classical methods like mathematical transformations spatial frequency-domain methods. Hence, recently, there has been an increasing interest nature-inspired optimization techniques processing purposes. The water cycle algorithm (WCA) is one algorithms (NIAs) that gotten much attention optimizing real-world problems due to its appealing performance. However, best author’s knowledge, little research undertaken WCA’s image-enhancing capacity. Thus, this work intended offer a modified histogram equalization (HE) approach WCA enhance contrast maintain brightness. Besides, proposed WCA-based technique was compared linear stretching (LCS), HE versions, particle swarm (PSO), accelerated (APSO). In addition objective function fitness, 11 full reference (FR) quality assessment (IQA) metrics were employed evaluate compare Experimental results showed suggested exhibited better performance than others enhancing dark grayscale images terms fitness perceptual visual IQA multi-scale structural similarity (MS-SSIM), information-weighted (IW-SSIM), mean squared error (IW-MSE), peak signal-to-noise ratio (IW-PSNR). method also demonstrated faster convergence time optimum solution.