Underwater Scene Enhancement via Adaptive Color Analysis and Multispace Fusion

Marine observation faces significant challenges due to the complex optical characteristics of underwater environments. The combined effects of light attenuation and scattering lead to severe image degradation, while the dynamic nature of underwater conditions introduces additional variability that traditional enhancement methods struggle to address. This work introduces a novel framework for underwater image enhancement that leverages multidomain analysis to achieve robust performance across diverse marine environments. The proposed method incorporates an adaptive scene analysis mechanism that determines optimal enhancement strategies based on environmental characteristics. The enhancement pipeline consists of two key components: First, a comprehensive degradation analysis module that addresses both illumination irregularities and wavelength-dependent attenuation through domain-specific compensation. Second, an advanced fusion scheme that combines multiple enhanced representations using an optimized pyramid model, enabling effective preservation of both global contrast and local details. The framework excels in challenging scenarios such as turbid waters and nonuniform lighting conditions, where conventional methods often fail. Extensive experiments conducted on multiple benchmark data sets demonstrate that our approach achieves superior performance in terms of both objective metrics and subjective visual quality. The results show consistent improvement in visibility, color fidelity, and detail preservation across varying underwater conditions, making it particularly suitable for marine observation and analysis tasks.