Underwater images often suffer from blurring and color distortion due to absorption and scattering in the water. Such effects are undesirable since they may hinder computer vision tasks. Many underwater image enhancement techniques have been explored to address this issue, each to varying degrees of success. The large variety of distortions in underwater images is difficult to handle by any singular method. This study observes four underwater image enhancement methods, i.e., Underwater Light Attenuation Prior (ULAP), statistical Background Light and Transmission Map estimation (BLTM), and Natural-based Underwater Image Color Enhancement (NUCE), and Global–Local Networks (GL-Net). These methods are evaluated on the Underwater Image Enhancement Benchmark (UIEB) dataset using quantitative metrics, e.g., SSIM, PSNR, and CIEDE2000 as the metrics. Additionally, a qualitative analysis of …