  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.
