摘要
Low-light image enhancement (LLIE) remains two fundamental challenges: accurately recovering fine details and effectively suppressing noise. Existing methods often struggle to strike a balance between these two objectives, resulting in results that are either overly smoothed or contaminated by excessive noise artifacts. Our motivation stems from the fact that during LLIE, noise predominantly manifests in the low-frequency components of the image and tends to propagate uniformly across the image. Leveraging this insight, we propose the Diffusion-based Laplacian Frequency-aware Network (DLFN), which decomposes images into multi-scale high- and low-frequency components using Laplacian decomposition. For low-frequency noise suppression, we exploit the denoising capability of diffusion models to eliminate noise introduced during the enhancement process effectively. To prevent over-smoothing and preserve fine details, we introduce the High-Frequency Enhancement Module (HFEM), which enhances high-frequency details across multiple Laplacian subbands, optimizing texture restoration. Additionally, we design the Multi-Scale Bilateral Fusion Module (MBFM) to facilitate the progressive fusion of high- and low-frequency features. Extensive experiments show that our method outperforms existing LLIE approaches.