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.
@article{arxiv.2009.06604,
title = {GIA-Net: Global Information Aware Network for Low-light Imaging},
author = {Zibo Meng and Runsheng Xu and Chiu Man Ho},
journal= {arXiv preprint arXiv:2009.06604},
year = {2021}
}