Enhanced underwater fish image processing by noise elimination, saturation adjustment, and edge sharpening technique

Analyzing underwater images and improving their quality is a difficult task for researchers. Processing underwater images is extremely difficult because of the low contrast, noise, and blurriness brought on by light scattering and absorption. Since the longer wavelengths of sunshine are unable to get deep into the water due to salinity and an abundance of dissolved pollutants, underwater image becomes a crucial research topic, but the photos appear blurry, noisy, and faded. By combining bilateral filtering for decreased noise, saturation improvement for color restoration, and Laplacian sharpening for clarity, this study suggests a novel method for improving underwater image quality. The datasets FishImg and Fish4Knowledge were used to test the approach. With an estimated Peak Signal-to-Noise Ratio (PSNR) of 45.38 dB, Structural Similarity Index (SSIM) of 0.9888, and Mean Squared Error (MSE) of 1.88, the empirical findings show a notable improvement in image quality over current methods. The method improves clarity while maintaining structural integrity, which makes underwater photos better suited for aquatic research, marine object detection, and environmental monitoring. All things considered, the suggested framework offers a workable and efficient way to enhance underwater image quality in actual applications.