Underwater Image Enhancement Based on Accelerated Conditional Diffusion Probabilistic Model

ABSTRACT Underwater images often suffer from significant color distortion and blurred features due to optical loss and dispersion. This degradation can hinder tasks such as underwater object detection. To address this issue, this study proposes an underwater image enhancement (UIE) model based on an accelerated conditional diffusion probabilistic model (UW‐DDPM). This model is a rapid denoising diffusion probabilistic model designed specifically for UIE. The UW‐DDPM directly establishes a diffusion generation relationship between degraded and reference images based on the conditional diffusion probabilistic model (CDDPM) redesigning an implicit accuracy diffusion model for direct image translation, which not only improves the quality of image enhancement but also addresses the slow sampling speed issue of the CDDPM. Simultaneously, speed‐UIE was designed for processing training on conditional images, which is a lightweight model network. Specifically, we combined a pre‐trained diffusion model with a lightweight UIE algorithm, using speed‐UIE to guide conditional generation. The diffusion prior mitigates the drawbacks of poor‐quality synthetic images, whereas the l