Frequency domain-based latent diffusion model for underwater image enhancement

Abstract

The degradation of underwater images, due to complex factors, negatively impacts the performance of underwater visual tasks. However, most underwater image enhancement methods (UIE) have been confined to the spatial domain, disregarding the frequency domain. This limitation hampers the full exploitation of the model’s learning and representational capabilities. To address this, a two-stage frequency domain-based latent diffusion model (FD-LDM) is introduced for UIE. Firstly, the model employs a lightweight parameter estimation network (L-PEN) to estimate the degradation parameters of underwater images, thereby mitigating the impact of color bias on the diffusion model. Subsequently, considering the varying degrees of recovery between high and low-frequency images, high and low-frequency priors are extracted in the second stage and integrated with the refined latent diffusion model to enhance the images further. Extensive experiments have confirmed the method’s effectiveness and robustness, particularly under color bias scenes.