用扩散模型修复水下照片的模糊、偏色问题，提升海洋摄影视觉效果。
LOBSTgER-enhance: an underwater image enhancement pipeline
- 通过合成退化数据训练扩散模型，逆向恢复水下图像质量。
- 在2500张照片上训练，1100万参数模型可生成512x768高清图。
- 适合海洋摄影爱好者与需要图像增强的科研人员使用。
水下摄影存在对比度降低、空间模糊和波长依赖性色彩失真等固有挑战，常使海洋生物活力难以展现，摄影师需大量后期处理。本文提出一种图像到图像的增强流水线，通过引入合成退化流程，利用基于扩散的生成模型学习逆转水下退化效应。训练与评估基于摄影师Keith Ellenbogen提供的高质量小规模数据集。所提方法在从头训练约2500张图像后，以约1100万参数模型实现512×768图像的高感知一致性与强泛化能力。
原文摘要 · Abstract (English)
Underwater photography presents significant inherent challenges including reduced contrast, spatial blur, and wavelength-dependent color distortions. These effects can obscure the vibrancy of marine life and awareness photographers in particular are often challenged with heavy post-processing pipelines to correct for these distortions. We develop an image-to-image pipeline that learns to reverse underwater degradations by introducing a synthetic corruption pipeline and learning to reverse its effects with diffusion-based generation. Training and evaluation are performed on a small high-quality dataset of awareness photography images by Keith Ellenbogen. The proposed methodology achieves high perceptual consistency and strong generalization in synthesizing 512x768 images using a model of ~11M parameters after training from scratch on ~2.5k images.
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