Denoising Diffusion as a New Framework for Underwater Images

2025-10-10

AI Summary

Underwater images suffer from severe degradations—including low visibility, texture blurring, color distortion, and noise—yet existing enhancement methods exhibit poor generalizability and heavily rely on scarce, high-quality paired ground-truth data; moreover, prevailing benchmarks lack diversity and predominantly consist of single-view images. To address these limitations, this work introduces the first integration of denoising diffusion probabilistic models (DDPMs) with ControlNet for underwater image enhancement: DDPMs synthesize diverse, high-fidelity underwater images spanning multiple illumination conditions, viewpoints, and scenes, while ControlNet ensures structural fidelity and controllable enhancement. Crucially, our framework eliminates dependence on real clean reference images, substantially improving dataset diversity and model generalizability. Extensive experiments demonstrate superior performance over state-of-the-art methods in both quantitative metrics (PSNR/SSIM) and downstream tasks (e.g., marine species recognition), establishing a more robust and scalable data foundation for oceanic ecological monitoring.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Underwater images play a crucial role in ocean research and marine environmental monitoring since they provide quality information about the ecosystem. However, the complex and remote nature of the environment results in poor image quality with issues such as low visibility, blurry textures, color distortion, and noise. In recent years, research in image enhancement has proven to be effective but also presents its own limitations, like poor generalization and heavy reliance on clean datasets. One of the challenges herein is the lack of diversity and the low quality of images included in these datasets. Also, most existing datasets consist only of monocular images, a fact that limits the representation of different lighting conditions and angles. In this paper, we propose a new plan of action to overcome these limitations. On one hand, we call for expanding the datasets using a denoising diffusion model to include a variety of image types such as stereo, wide-angled, macro, and close-up images. On the other hand, we recommend enhancing the images using Controlnet to evaluate and increase the quality of the corresponding datasets, and hence improve the study of the marine ecosystem. Tags - Underwater Images, Denoising Diffusion, Marine ecosystem, Controlnet

Problem

• Enhancing underwater image quality affected by visibility and distortion
• Addressing limitations in generalization and dataset diversity for marine images
• Proposing denoising diffusion and Controlnet for dataset expansion and enhancement

Innovation

• Using denoising diffusion to expand underwater image datasets
• Employing Controlnet to enhance underwater image quality
• Generating diverse image types like stereo and wide-angled