Abstract
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: this https URL.
Abstract (translated)
水下环境中的活动在多个场景中至关重要，这推动了持续发展水下图像增强技术的需求。该领域的主要挑战在于拍摄图像的深度，随着深度增加，光线变暗。大多数现有的水下图像增强方法主要集中在噪声去除和色彩调整上，而很少有研究致力于亮度提升。这项工作介绍了一种使用扩散模型进行水下图像增强的新颖无监督学习方法。我们的方法称为UDBE（Underwater Diffusion Brightness Enhancement），基于条件扩散来保持未配对输入图像的亮度细节。通过将输入图像与色彩图和信噪比图(SNR)相结合，确保了稳定训练并防止输出图像中的色彩失真。 实验结果表明，在UIEB、SUIM和RUIE这些公认的水下图像基准数据集上，我们的方法取得了令人印象深刻的准确率。此外，根据图像质量指标PSNR（峰值信噪比）、SSIM（结构相似性指数）、UIQM（综合水下图象质量度量）和UISM（水下图像结构-美度模型），实验验证了我们方法的鲁棒性和良好的亮度提升性能。 源代码可在此处获取：[请在原文链接中查看具体网址]
URL
https://arxiv.org/abs/2501.16211
