Underwater Image Enhancement via Dehazing and Color Restoration

TL;DR

This paper introduces WaterFormer, a ViT-based network that separately models haze and color degradation in underwater images, achieving superior enhancement by dynamically fusing these features with physics-based reconstruction.

Contribution

The paper presents a novel ViT-based architecture with decoupled haze and color restoration modules, and a physics-informed reconstruction layer for underwater image enhancement.

Findings

Outperforms state-of-the-art methods in quality metrics

Effectively preserves color fidelity and structural details

Demonstrates robustness across diverse underwater conditions

Abstract

Underwater visual imaging is crucial for marine engineering, but it suffers from low contrast, blurriness, and color degradation, which hinders downstream analysis. Existing underwater image enhancement methods often treat the haze and color cast as a unified degradation process, neglecting their inherent independence while overlooking their synergistic relationship. To overcome this limitation, we propose a Vision Transformer (ViT)-based network (referred to as WaterFormer) to improve underwater image quality. WaterFormer contains three major components: a dehazing block (DehazeFormer Block) to capture the self-correlated haze features and extract deep-level features, a Color Restoration Block (CRB) to capture self-correlated color cast features, and a Channel Fusion Block (CFB) that dynamically integrates these decoupled features to achieve comprehensive enhancement. To ensure authenticity, a soft reconstruction layer based on the underwater imaging physics model is included. Further, a Chromatic Consistency Loss and Sobel Color Loss are designed to respectively preserve color fidelity and enhance structural details during network training. Comprehensive experimental results demonstrate that WaterFormer outperforms other state-of-the-art methods in enhancing underwater images.