Progressive Colour Equalisation and Detail Refinement for Underwater Image Enhancement

Abstract

Underwater image enhancement remains a critical challenge in computational vision due to complex distortions caused by wavelength-dependent light absorption and scattering. This paper introduces CEDFNet, a novel two-stage framework that leverages advanced computational intelligence techniques for robust and high-fidelity underwater image restoration. The first stage integrates a Colour Equalisation Transformer (CET) to perform global colour correction by modelling long-range dependencies and mitigating dominant hue distortions. The second stage combines a Residual Texture Modulation Adaptor (RTMA) with an Enhanced Bilateral Enhancement Decoder (EBED) to refine structural details and enhance local contrast through context-aware and adaptive feature learning. Extensive evaluations on benchmark datasets including UIEBD, LSUI, and Colour-Checker7 validate the superiority of CEDFNet over existing state-of-the-art approaches. Quantitatively, CEDFNet achieves significant improvements across multiple perceptual and fidelity metrics such as PSNR, SSIM, FID, and LPIPS. Comprehensive ablation studies further confirm the complementary roles of CET, RTMA, and EBED, whereas parameter sensitivity analyses highlight the framework's robust and stable behaviour. By integrating transformer-based global correction with task-adaptive local enhancement, CEDFNet advances the frontier of underwater image restoration in the domain of computational intelligence. It generalises well across diverse imaging conditions and offers a lightweight and end-to-end solution suitable for real-world deployment in marine robotics, inspection, and visual perception systems.