Dynamic SpectraFormer for Ultra-High-Definition Underwater Image Enhancement

2026-08-19

AI Summary

本文针对水下图像因光线折射和吸收导致的颜色失真、雾霾及可见度低的问题，提出了一种基于频域变换的Dynamic SpectraFormer方法，有效提升了图像质量。

Abstract

Underwater images suffer from color distortion, haze, and poor visibility due to light refraction and absorption in water. These challenges significantly impact the utilization of Autonomous Underwater Vehicles (AUVs) or marine robots. Typically, color and brightness distortions manifest at lower frequencies, while edge and texture distortions are prevalent at higher frequencies. Traditional methods struggle to concurrently rectify these mixed distortions as they primarily concentrate on the spatial domain. To address these issues, we introduce the Dynamic SpectraFormer, which enhances underwater images through a frequency domain transformer. The Dynamic SpectraFormer introduces an ultra-high-resolution sparse spectrum attention module, which could capture the long-term dependency without losing the universal approximating power. Additionally, we have developed a dynamic spectrum weight generation layer that serves as an adaptive spectrum band selector, accentuating critical frequency bands and suppressing less relevant ones. Consequently, this method significantly improves underwater image quality by addressing both high- and low-frequency distortions. Our extensive ablation studies and comparative evaluations consolidate the Dynamic SpectraFormer's efficacy across multiple underwater image enhancement benchmarks. The source code is available at https://github.com/arifence2024/DynamicSpectraFormer.git.

Problem

• Underwater Image
• Color Distortion
• Haze
• Visibility
• Frequency Domain

Innovation

• Dynamic SpectraFormer
• ultra-high-resolution sparse spectrum attention
• dynamic spectrum weight generation layer