Underwater Quality Enhancement Based on Mixture Contrast Limited Adaptive Histogram and Multiscale Fusion

This paper presents a novel approach for enhancing the visual quality of underwater images using various spatial processing techniques. This research addresses the common issues encountered in underwater imaging, such as color distortion, low clarity, low contrast, bluish or greenish tints caused by light scattering and absorption, and the presence of underwater organisms. To solve these problems, we utilize various image processing methods such as white balancing, Contrast Limited Adaptive Histogram Equalization (CLAHE) in Lab and HSV color spaces, sharpening, weight map generation, and multiscale fusion. The effectiveness of the proposed approach is evaluated quantitatively using mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). The results indicate that the optimal CLAHE parameters are a block size 4x4 and a clip limit 1.2. These parameters yielded an MSE value of 0.7594, a PSNR value of 20.7121, and an SSIM value of 0.8826, demonstrating superior performance compared to previous research. A qualitative evaluation was also conducted using eight respondents based on overall visual quality, color fidelity, and contrast enhancement. The assessment results demonstrate satisfactory outcomes, with a mean score of 4.3278 and a standard deviation of 0.7238. Overall, this research demonstrates that effective and efficient enhancement of underwater image quality through computational methods can be achieved using simple techniques with appropriate parameters and placement, thereby enabling better scientific research and exploration of the underwater world.