Underwater Image Enhancement via Dehazing and Color Restoration

2024-09-15

arXiv.org

AI Summary

Underwater images commonly suffer from low contrast, blurriness, and chromatic distortion. Existing methods often couple haze removal and color correction in a single model, neglecting their physical independence and synergistic interaction. This paper proposes WaterFormer, a decoupled Vision Transformer architecture: it employs dedicated dehazing and color restoration blocks to model these two degradation processes separately, and introduces a channel fusion block for dynamic inter-block coordination. A soft reconstruction layer, grounded in the underwater imaging physical model, is incorporated to enhance fidelity. Furthermore, we propose a joint optimization strategy combining chromatic consistency loss and Sobel-based color loss to simultaneously preserve color accuracy and structural details. Extensive experiments on multiple benchmark datasets demonstrate that WaterFormer achieves state-of-the-art performance in PSNR, SSIM, and human perceptual evaluation—significantly improving image contrast, sharpness, and color fidelity.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Web data integration and cleaning
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Enhance underwater images with low contrast and blurriness
• Address independent yet synergistic haze and color degradation
• Improve color fidelity and structural details in underwater imaging

Innovation

• ViT-based WaterFormer for underwater image enhancement
• DehazeFormer and CRB blocks for independent feature extraction
• Chromatic Consistency Loss to preserve color fidelity