# Hybrid Underwater Image Enhancement via Dual Transmission Optimization and Transformer-Based Feature Fusion

## TL;DR

This paper introduces a new method for improving underwater image quality by combining transmission estimation and a transformer-based network.

## Contribution

The novel hybrid approach integrates dual transmission optimization and a U-Net Transformer for enhanced underwater image restoration.

## Key findings

- The method effectively addresses visibility degradation using boundary constraints and local contrast.
- Adaptive ambient light estimation and color correction robustly correct color distortion.
- Experiments show superior performance compared to state-of-the-art methods using UIQM and UCIQE metrics.

## Abstract

Due to complex underwater environments characterized by severe scattering, absorption, and color distortion, accurate restoration remains challenging. This paper proposes a hybrid approach combining dual transmission estimation, adaptive ambient light estimation with color correction, and a U-Net Transformer (Uformer) for underwater image enhancement. Our method estimates transmission maps by integrating boundary constraints and local contrast, which effectively address visibility degradation. An adaptive ambient light estimation and color correction strategy are further developed to correct color distortion robustly. Subsequently, a Uformer network enhances the restored image by capturing global and local contextual features effectively. Experiments conducted on publicly available underwater image datasets validate our approach. Performance is quantitatively evaluated using widely adopted non-reference image quality metrics, especially Underwater Image Quality Measure (UIQM) and Underwater Color Image Quality Evaluation (UCIQE). The results demonstrate that our proposed method achieves superior enhancement performance over several state-of-the-art methods.