A robust underwater image enhancement algorithm

Capturing clear images in underwater environments is a major challenge in marine engineering. There are many issues to consider in obtaining clear underwater images such as climate, environment, and human factors. The most important reasons are the atomization effect caused by dispersion and the color cast caused by inconsistent energy attenuation of each wavelength when light propagates in water. Recently, deep learning technology has shown impressive performance on underwater image enhancement. The deep learning-based methods apply to the underwater image enhancement tasks. We propose a deep learning model for inferring a degradation model to further improve image dynamic range through a network-guided underwater image enhancement network architecture with multicolor space embedding and convolutional media transfer, fixed an issue with limited dynamic range and brightness in underwater images. Quantitative and qualitative results show that our network performs relatively well in the Underwater Image Enhancement Benchmark (UIEB) [7] dataset compared to other recent methods, and is expected to be applied to different types of underwater work and environments in the future and reduce the degradation problems that often occur with underwater images. The acquisition of high-fidelity imagery in subaqueous environments presents significant technical challenges in marine engineering, encompassing a complex interplay of climatological variables, environmental parameters, and anthropogenic factors. Primary impediments to image clarity comprise the atomization phenomenon induced by optical scattering and chromatic distortion resulting from wavelength-dependent energy attenuation in aqueous media. The procurement of high-resolution underwater imagery is fundamental to numerous scientific applications, including marine biological research, autonomous underwater robotics, and environmental surveillance systems, where precise visual data acquisition substantially augments analytical efficacy. Contemporary developments in deep learning architectures have exhibited remarkable potential for enhancing underwater image quality. In response to these challenges, we present a novel deep learning framework that derives an empirical degradation model, utilizing a network-guided enhancement architecture incorporating multicolor space embedding and convolutional media transfer methodologies to optimize image dynamic range. This methodological approach specifically addresses the limitations in luminance distribution and dynamic range characteristics inherent in subsea imagery. Empirical evaluation of our architectural framework on the standardized Underwater Image Enhancement Benchmark (UIEB) [7] dataset demonstrates statistically significant performance improvements over contemporary methodologies, suggesting broad applicability across diverse submarine environments for mitigating common degradation phenomena.