Learning Semantic-aware Retinex Network with Spatial-Frequency Interaction for Low-light Image Enhancement

Hao Li, Jinlong Wang, Hanxiang Yang, Xiongxin Tang, Fanjiang Xu

Abstract: Retinex-based methods have achieved significant progress in enhancing low-light images benefits for its disentanglement property. However, existing methods either ignore the semantic priors or randomly leverage them only in the spatial domain, which leads to insufficient coupling and limits the performance gains. Considering the lightness mainly exists in the amplitude component and the rest is related to the phase component, making it optimal to combine the Retinex decomposition with the Fourier transform to achieve customized restoration. In this paper, we propose a novel method RetinexFour tailored for low-light image enhancement. Specifically, it consists of Phase-Guided Multi-head Self-Attention (PG-MSA) and Local Spatial Attention (LSA) to allow for the reconstruction of structure from spatial-frequency perspectives. To achieve exposure correction, we introduce Selective Amplitude feature Fusion (SAFF) by combining the original and complementary amplitude to achieve global lightness adjustment. Extensive experiments demonstrate the superiority of our method over other SOTA methods on four benchmark datasets.