Incorporating Fourier Transformation With Diffusion Models for Low-Light Image Enhancement

In this letter, we propose a diffusion-based framework that leverages the generative ability of diffusion models and the advantages of the physically explainable Fourier transformation for visually satisfactory low-light image enhancement. Specifically, we first employ an encoder to convert the paired low-light and normal-light images into latent features and transform the features into the frequency domain through Fourier transformation, resulting in amplitude components that contain illumination information and phase components that represent details information. Subsequently, we present the latent-Fourier diffusion model which performs diffusion operations on the phase components for details reconstruction. Furthermore, we propose a lightness boost module to reconstruct amplitude aiming to improve the contrast in the frequency domain, and the restored feature obtained by performing inverse Fourier transformation on the reconstructed phase and amplitude components is further refined by the proposed latent feature fusion module to achieve better visual perception. Finally, the refined feature is taken as input to a decoder to produce the final restored image. Extensive experiments on publicly available benchmarks demonstrate our proposed method outperforms state-of-the-art competitors.