Shared but Not Substitutable: Frequency-Aware Color–Intensity Collaboration for Low-Light Image Enhancement

We propose SPARNet, a frequency-aware framework for low-light image enhancement that explicitly models shared and component-specific information between chromatic and intensity representations in the HVI color space. Motivated by the observation that low-frequency HV and I components exhibit increasing similarity at coarser scales while remaining weakly predictable from one another under linear mappings, SPARNet learns shared and private representations to exploit cross-component dependencies without compromising their complementary characteristics. Specifically, a three-level wavelet pyramid captures multi-scale low-frequency context, while shared–private latent fusion enables adaptive information exchange and preserves component-specific representations through explicit regularization. The fused low-frequency context further guides directional high-frequency subband correction, facilitating illumination restoration, detail recovery, and noise suppression. Experiments on paired and unpaired low-light benchmarks show that SPARNet achieves the best results in eight of nine metric–dataset combinations across the LOL benchmarks and the lowest average NIQE on five unpaired datasets. Ablation studies and representation analyses further demonstrate the effectiveness of shared–private frequency collaboration.