<p>Recently, many restoration and enhancement methods for underwater images have emerged. However, the existing methods may be time-consuming and labor-intensive. To address these issues, we propose a systematic and efficient strategy for restoring and enhancing underwater images in this study. The importance of accurately estimating background light (BL) and transmission map (TM) cannot be overstated in underwater image enhancement. BL estimation is crucial as it directly influences the removal of the haze-like effect caused by water absorption and scattering. Similarly, the TM, which describes the portion of light that reaches the camera after being reflected by the scene objects, is essential for recovering the true colors and details of the underwater scene. According to the physical imaging model, a novel joint maximum prior (JMP) is proposed to search for background light (BL). The JMP reveals the internal relevance between scene depth and candidate regions of BL. Notably, the genetic algorithm (GA) is embedded in our method to determine BL solutions via a valid fitness function. Innovatively, the color space is converted from RGB to HSI (Hue, Saturation, Intensity), and component I is set as the initial transmission map (TM) of the R channel. For an improved estimation of TM, a balance compensation scheme (BCS) is carried out. It’s worth mentioning that numerous algorithms employ a fixed light wavelength to calculate the attenuation coefficient. In contrast, based on the wavelength function, we use a global attenuation coefficient (GAC) to increase the fault tolerance. Unlike some previous methods that focused on simple post-processing steps, we present an adaptive color adjustment method (ACAM) to increase global quality while retaining local details in recovered images. The experimental findings demonstrate that the proposed strategy is successful.</p>