2026-08-19 · IROS · 3 citations

Dynamic SpectraFormer for Ultra-High-Definition Underwater Image Enhancement

Zhiqiang Hu, Tao Yu, Shouren Huang, Masatoshi Ishikawa

Published at IROS (the arXiv record still lists it as a preprint). 3 citations, as of the last refresh.

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.

arXiv comment: 8 pages, 7 figures. Published in the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)

Ten-minute slide kit

Six slides is the whole talk: what was broken, what people tried, what these authors did, what the numbers say, where it falls over, and the sentence people should remember.

SLIDE 1

The problem

The degradation in underwater image quality hampers the visual sensing capabilities of marine robots, despite that they are equipped with high-end cameras. Thus, algorithms for Underwater Image Enhancement (UIE) play a critical role in advancing aquatic exploration, with widespread applications in domains like Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs). This degradation in image quality is primarily due to the wavelengthdependent scattering and attenuation of light as it travels…

SLIDE 2

What came before

Not recoverable from the parsed text. Read this section in the paper yourself.

SLIDE 3

The method

In this section, we present the proposed Dynamic SpectraFormer in detail. After briefly introducing the overall architecture as shown in Fig. 2, we present the Dynamic SpectraFormer block. Our work aims to harness the power of DCT to boost the performance of Transformer models for high-definition underwater image enhancement tasks. Quantitative Evaluation To consolidate our performance superiority, we compare our SpectraFormer with multiple UIE methods, including Ucolor , WaterNet , UGAN , FUnIE-GAN , Deep SESR ,…

SLIDE 4

What they measured

The LSUI dataset was segmented into 4500, and 404 images for training and testing, respectively. In the evaluation stage, we also conducted assessments using the underwater image sets from UIEB (90 pairs) , LSUI (504 pairs) , and EUVP (515 pairs) , respectively. Evaluation metrics include Peak Signal Noise Ratio (PSNR) and Structural Similarity Index (SSIM), measuring the color and structural fidelity between enhanced images and ground

SLIDE 5

Where it breaks

Not recoverable from the parsed text. Read this section in the paper yourself.

SLIDE 6

One-line takeaway

In this paper, we proposed Dynamic SpectraFormer, a highly efficient frequency domain transformer that could enhance the UHD underwater image in the frequency domain and empower it with adaptability to the contents. Extensive analysis conducted on real-world datasets, along with comprehensive ablation studies, validates the efficacy of the proposed method for UHD underwater image enhancement

Assembled from the paper's own PDF, parsed with its layout intact so tables and equations survive, 43,972 characters of it, then split on the paper's own section headings. Extractive, not generated: every sentence here is lifted from the paper. Check it before you present it.