Low-Light Image Enhancement via Wavelet Domain Frequency Cross-Attention

This study proposes a novel low-light image enhancement network that incorporates a frequency cross-attention mechanism in the wavelet domain. The proposed network enhances brightness through the low-frequency wavelet subband while simultaneously restoring fine details in the high-frequency subbands. Color degradation during the brightening process is prevented by applying a color-preserving block based on the saturation component before the illumination adjustment. Furthermore, U-shaped lightening and multiscale sharpening blocks are designed to enhance the image brightness and detail, respectively. The disruption of intrinsic symmetry in coefficient correlations poses a major challenge in independently processing wavelet subbands. To address this issue, we propose a frequency cross-attention block that enables effective information exchange between subbands, thereby preserving their inherent correlations. The proposed network produces visually consistent and refined outputs by balancing the enhanced wavelet subbands. Experimental evaluations demonstrate that the proposed network achieves competitive performance in both subjective quality and objective metrics, confirming its effectiveness for low-light image enhancement.