A Patch-Based Method for Underwater Image Enhancement With Denoising Diffusion Models.

The enhancement of underwater images has emerged as a significant technological challenge in advancing marine research and exploration tasks. Due to the scattering of suspended particles and absorption of light in underwater environments, underwater images tend to present blurriness and predominantly color distortion. In this study, we propose a novel approach utilizing denoising diffusion models to improve underwater degraded images. After training the noise estimation network of the denoising diffusion models, we accelerate the deterministic sampling process with denoising diffusion implicit models. We also propose a patch-based method by implementing average sampling between overlapping image patches at each sampling step, enabling the generation of images at arbitrary resolution while preserving their natural appearance and details. Through benchmark experiments, we illustrate that our method outperforms or closely approaches state-of-the-art techniques in terms of effectiveness and performance. We demonstrate that our approach reduces the interference of underwater environments with the semantic information of the images by salient object detection experiments.