用扩散模型无监督提升水下图像亮度，保留细节不偏色。
UDBE: Unsupervised Diffusion-based Brightness Enhancement in Underwater Images
- 基于条件扩散模型，结合颜色图与信噪关系图增强亮度。
- 在UIEB、SUIM、RUIE数据集上表现优异，PSNR、SSIM等指标领先。
- 无需配对数据，适合水下摄影、海洋探测等真实场景应用。
水下环境活动在多个场景中至关重要，推动了水下图像增强技术的持续发展。主要挑战在于拍摄深度增加导致环境变暗。现有方法多聚焦于去噪和色彩校正，少有工作关注亮度增强。本文提出一种新型无监督学习方法UDBE，基于扩散模型实现水下图像亮度增强。该方法采用条件扩散机制，将输入图像与颜色图、信噪比（SNR）图结合，确保训练稳定并防止输出图像出现色彩失真。实验结果表明，UDBE在三个主流水下图像基准数据集UIEB、SUIM和RUIE上均取得优异性能，各项图像质量指标（如PSNR、SSIM、UIQM、UISM）验证了其亮度增强的有效性与鲁棒性。代码已开源：https://github.com/gusanagy/UDBE。
原文摘要 · Abstract (English)
Activities in underwater environments are paramount in several scenarios, which drives the continuous development of underwater image enhancement techniques. A major challenge in this domain is the depth at which images are captured, with increasing depth resulting in a darker environment. Most existing methods for underwater image enhancement focus on noise removal and color adjustment, with few works dedicated to brightness enhancement. This work introduces a novel unsupervised learning approach to underwater image enhancement using a diffusion model. Our method, called UDBE, is based on conditional diffusion to maintain the brightness details of the unpaired input images. The input image is combined with a color map and a Signal-Noise Relation map (SNR) to ensure stable training and prevent color distortion in the output images. The results demonstrate that our approach achieves an impressive accuracy rate in the datasets UIEB, SUIM and RUIE, well-established underwater image benchmarks. Additionally, the experiments validate the robustness of our approach, regarding the image quality metrics PSNR, SSIM, UIQM, and UISM, indicating the good performance of the brightness enhancement process. The source code is available here: https://github.com/gusanagy/UDBE.
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