HVI-Based Spatial–Frequency-Domain Multi-Scale Fusion for Low-Light Image Enhancement

Low-light image enhancement aims to restore images captured under extreme low-light conditions. Existing methods demonstrate that fusing Fourier transform magnitude and phase information within the RGB color space effectively improves enhancement results. Meanwhile, recent advances have demonstrated that certain color spaces based on human visual perception, such as Hue–Value–Intensity (HVI), are superior to RGB for enhancing low-light images. However, these methods neglect the key impact of the coupling relationship between spatial and frequency-domain features on image enhancement. This paper proposes a spatial–frequency-domain multi-scale fusion for low-light image enhancement by exploring the intrinsic relationships among the three channels of HVI space, which consists of a dual-path parallel processing architecture. In the spatial domain, a specifically designed multi-scale feature extraction module systematically captures comprehensive structural information. In the frequency domain, our model establishes deep coupling between spatial features and Fourier transform features in the I-channel. The effectively fused features from both domains synergistically drive an encoder–decoder network to achieve superior image enhancement performance. Extensive experiments on multiple public benchmark datasets show that the proposed method significantly outperforms state-of-the-art approaches in both quantitative metrics and visual quality.