用真人评价训练AI，让水下照片又清又真。
Enhancing Underwater Images Using Deep Learning with Subjective Image Quality Integration
- 把人眼判断好坏的标准融入训练，指导AI优化图像
- 结合色彩真实度和清晰度后，图像质量显著提升
- 适合做水下摄影、海洋探测的图像增强
深度学习在包括水下图像自动增强在内的多个领域产生深远影响。本文提出一种基于深度学习的方法，通过将人类主观评价融入训练过程来提升水下图像质量。利用公开数据集中的专家标注图像（高/低质量），先训练分类网络区分图像优劣，再以多种增强标准训练生成对抗网络（GAN）以优化低质量图像。模型性能通过PSNR、SSIM、UIQM等定量指标及定性分析评估。结果表明，尤其在引入色彩保真度和图像锐度等标准时，所提方法在感知质量和测量指标上均实现显著提升。
原文摘要 · Abstract (English)
Recent advances in deep learning, particularly neural networks, have significantly impacted a wide range of fields, including the automatic enhancement of underwater images. This paper presents a deep learning-based approach to improving underwater image quality by integrating human subjective assessments into the training process. To this end, we utilize publicly available datasets containing underwater images labeled by experts as either high or low quality. Our method involves first training a classifier network to distinguish between high- and low-quality images. Subsequently, generative adversarial networks (GANs) are trained using various enhancement criteria to refine the low-quality images. The performance of the GAN models is evaluated using quantitative metrics such as PSNR, SSIM, and UIQM, as well as through qualitative analysis. Results demonstrate that the proposed model -- particularly when incorporating criteria such as color fidelity and image sharpness -- achieves substantial improvements in both perceived and measured image quality.
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