It is extremely challenging to acquire perceptually plausible images under
low-light conditions due to low SNR. Most recently, U-Nets have shown promising
results for low-light imaging. However, vanilla U-Nets generate images with
artifacts such as color inconsistency due to the lack of global color
information. In this paper, we propose a global information aware (GIA) module,
which is capable of extracting and integrating the global information into the
network to improve the performance of low-light imaging. The GIA module can be
inserted into a vanilla U-Net with negligible extra learnable parameters or
computational cost. Moreover, a GIA-Net is constructed, trained and evaluated
on a large scale real-world low-light imaging dataset. Experimental results
show that the proposed GIA-Net outperforms the state-of-the-art methods in
terms of four metrics, including deep metrics that measure perceptual
similarities. Extensive ablation studies have been conducted to verify the
effectiveness of the proposed GIA-Net for low-light imaging by utilizing global
information.

本研究提出了一种全局信息感知模块 (GIA)，该模块能够提取和集成全局信息以改善低光成像表现，通过将 GIA 模块插入到基本 U-Net 中，构建了 GIA-Net，并在大规模真实世界低光成像数据集上实现了训练和评估，实验结果显示，所提出的 GIA-Net 在四个指标中表现优于现有的最先进方法。