Bidirectional cross-frequency guided wavelet diffusion for detail-preserving low-light image enhancement.

Low-light image enhancement is challenging due to noise and poor visibility. While diffusion models have shown remarkable generative capabilities, applying them directly to this task in the pixel domain remains problematic. They tend to struggle with the trade-off between global illumination correction and local detail preservation, often resulting in unnatural textures or amplified noise due to the lack of frequency-specific constraints. To address these limitations, we propose a novel Bidirectional Cross-Frequency Guided Wavelet Diffusion model (BCF-Diff), which operates in the frequency domain for efficient and detail-preserving enhancement. Our approach uses K-level Discrete Wavelet Transform to decompose images. We introduce a two-pronged strategy to tackle the core challenges. First, we focus the diffusion model exclusively on restoring the compact low-frequency component for efficiency and stability with a detail-weighted loss mechanism. This mechanism computes weights from the high-frequency components to encourage the model to prioritize the reconstruction of detail-rich regions, ensuring a high-quality structural base. Then, we propose an innovative Cross-Frequency Gated Refinement (CFGR), which leverages the newly restored low-frequency component as a guidance to direct the enhancement of high-frequency details, and subsequently employs a Spatial Gated Refinement Module to adaptively suppress noise and sharpen textures. Our bidirectional cross-frequency guidance ensures that textures and edges are generated coherently with the overall scene structure. Experiments show that BCF-Diff achieves competitive overall performance and particularly strong structural similarity on the evaluated paired datasets, while providing visually detailed enhancement results under low-light conditions.