🤖 AI Summary
This work proposes a diffusion-based image enhancement method to address the challenges of underwater photography, including low contrast, spatial blur, and wavelength-dependent color distortion caused by light absorption and scattering. The approach uniquely integrates a physics-inspired synthetic underwater degradation pipeline with a diffusion generative model, enabling the learning of an inverse mapping of the degradation process. Trained from scratch on only approximately 2.5k high-quality images, the resulting 11-million-parameter model demonstrates exceptional perceptual fidelity and generalization capability at a resolution of 512×768, significantly improving the visual quality of underwater imagery.
📝 Abstract
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.