Low-light image enhancement (LLIE) aims at improving the perception or interpretability of an image captured in an environment with poor illumination. With the advent of deep learning, the LLIE technique has achieved significant breakthroughs. However, existing LLIE methods either ignore the important role of frequency domain information or fail to effectively promote the propagation and flow of information, limiting the LLIE performance. In this paper, we develop a novel frequency-spatial interaction-driven network (FSIDNet) for LLIE based on two-stage architecture. To be specific, the first stage is designed to restore the amplitude of low-light images to improve the lightness, and the second stage devotes to restore phase information to refine fine-grained structures. Considering that Frequency domain and spatial domain information are complementary and both favorable for LLIE, we further develop two frequency-spatial interaction blocks which mutually amalgamate the complementary spatial and frequency information to enhance the capability of the model. In addition, we construct the Information Exchange Module (IEM) to associate two stages by adequately incorporating cross-stage and cross-scale features to effectively promote the propagation and flow of information in the two-stage network structure. Finally, we conduct experiments on several widely used benchmark datasets (i.e., LOL-Real, LSRW-Huawei, etc.), which demonstrate that our method achieves the excellent performance in terms of visual results and quantitative metrics while preserving good model efficiency.

翻译：低光照图像增强（LLIE）旨在提升在光照不足环境下捕获图像的感知度或可解释性。随着深度学习的兴起，LLIE技术取得了显著突破。然而，现有LLIE方法要么忽略了频域信息的重要作用，要么未能有效促进信息的传播与流动，从而限制了LLIE的性能。本文基于两阶段架构，提出了一种新颖的频率-空间交互驱动网络（FSIDNet）用于LLIE。具体而言，第一阶段旨在恢复低光照图像的幅度信息以改善亮度，第二阶段致力于恢复相位信息以细化细粒度结构。考虑到频域与空间域信息具有互补性且均有利于LLIE，我们进一步设计了两个频率-空间交互模块，通过相互融合互补的空间与频率信息来增强模型能力。此外，我们构建了信息交换模块（IEM），通过充分整合跨阶段与跨尺度特征来关联两个阶段，从而有效促进两阶段网络结构中信息的传播与流动。最后，我们在多个广泛使用的基准数据集（如LOL-Real、LSRW-Huawei等）上进行了实验，结果表明我们的方法在视觉结果和定量指标方面均取得了优异性能，同时保持了良好的模型效率。