Underwater Image Color Constancy Enhancement Network Based on Adaptive Correction Factor

This paper introduces an advanced Underwater Image Enhancement (UIE) framework that integrates multi-level color correction with multi-scale restoration to address the challenges of color distortion and image quality degradation in underwater environments caused by complex lighting variations and haze effects. Central to the framework is the Multi-Level Color Correction Model with Adaptive Factor (MLCC-AF), which leverages the principle of color constancy to estimate light color across multiple levels. The model dynamically adjusts global color balance, corrects local highlight regions, and redistributes channel color energy through an adaptive correction factor, effectively mitigating color deviations and significantly enhancing color fidelity and visual quality. Complementing this, the Multi-Scale Joint Restoration Network (MJRN) and Residual-Based Detail Enhancement Network (RDEN) are proposed to tackle haze effects and recover lost details. MJRN optimizes the dehazing process through joint parameter estimation, while RDEN adaptively enhances critical image features, ensuring superior clarity and detail preservation. Extensive experiments conducted on both reference and non-reference underwater image datasets demonstrate that the proposed method consistently outperforms existing state-of-the-art approaches in terms of color correction, contrast enhancement, and detail restoration. The results underline the method’s efficiency and robustness, offering a promising solution for UIE applications.