用去噪扩散模型扩充水下图像数据集并提升质量。
Denoising Diffusion as a New Framework for Underwater Images
- 用去噪扩散模型生成多样水下图像，扩展数据多样性。
- 通过Controlnet增强图像质量，提升数据集可用性。
- 适合海洋生态研究与水下视觉任务的开发者使用。
水下图像在海洋研究和海洋环境监测中至关重要，能提供生态系统的关键信息。然而，复杂的远程环境导致图像质量差，存在可见度低、纹理模糊、色彩失真和噪声等问题。近年来，图像增强方法虽有效，但存在泛化能力差、依赖高质量干净数据集等局限。现有数据集普遍存在多样性不足、质量低的问题，且多为单目图像，难以覆盖不同光照条件和视角。本文提出新方案：一方面，利用去噪扩散模型生成包括立体、广角、微距和近景在内的多样化水下图像，扩充数据集；另一方面，采用Controlnet对图像进行增强，提升数据集质量，从而促进海洋生态系统研究。该方法可显著改善数据集的代表性和可用性。
原文摘要 · Abstract (English)
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
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