Underwater Image Enhancement via Adaptive Bi-Level Color-Based Adjustment

Abstract

Underwater images often exhibit severe color distortions and reduced contrast due to light absorption and scattering, presenting substantial challenges for image enhancement techniques. To address these challenges, this article presents BCTA-Net, an adaptive bi-level color-based network specifically engineered to enhance the quality of underwater images by addressing distortions in dynamic and complex environments. The network integrates content-aware global and local restoration strategies. On a local scale, a color-aware attention mechanism is proposed which employs color histograms to adaptively correct nonuniform color distortions and enhance local color fidelity. In addition, a triple attention (TA) module restores spatially varying local details in a content-aware manner, improving clarity and texture precision of enhancement. These elements are combined into a dual-branch architecture aimed at reducing local contrast, color fidelity, and detail precision issues. On a global scale, contrastive learning focused on background lightness corrects color distortions due to uneven illumination. The integration of these components results in a lightweight, dynamic global-local model with robust generalization capabilities across various underwater scenarios, as demonstrated by comprehensive experiments that show significant performance improvements over existing methods.