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
Abstract (translated)
水下图像在海洋研究和海洋环境监测中扮演着重要角色，因为它们提供了关于生态系统质量的信息。然而，由于复杂的海底环境特点，所获得的图像通常质量较差，存在能见度低、模糊纹理、色彩失真和噪声等问题。近年来，有关图像增强的研究已经证明是有效的，但同时也带来了自身的局限性，比如泛化能力差以及对高质量数据集的高度依赖。其中一个挑战在于这些数据集中缺少多样性和包含的图像质量较低的问题。此外，大多数现有的数据集仅包括单目图像，这限制了不同光照条件和角度下的表现力。 在本文中，我们提出了一种新的策略来克服上述局限性。一方面，建议利用去噪扩散模型扩展数据集，并纳入多种类型的图像，例如立体图像、广角图像、微距图像以及特写图像等。另一方面，则推荐使用Controlnet增强这些图像的质量以评估和提高相应数据集的整体质量，从而提升对海洋生态系统的研究。 标签 - 水下图像, 去噪扩散模型, 海洋生态系统, Controlnet
URL
https://arxiv.org/abs/2510.09934
