论文标题
GIA-NET：低光成像的全球信息意识网络
GIA-Net: Global Information Aware Network for Low-light Imaging
论文作者
论文摘要
由于低SNR而导致的低光条件下，获得感知上合理的图像非常具有挑战性。最近，U-NET显示出低光成像的有希望的结果。但是，由于缺乏全球颜色信息，香草U-nets生成具有颜色不一致之类的文物的图像。在本文中，我们提出了一个全球信息意识（GIA）模块，该模块能够将全局信息提取和集成到网络中，以提高低光成像的性能。 GIA模块可以插入具有可忽略的额外可学习参数或计算成本的香草U-NET中。此外，在大规模的现实世界低光成像数据集上构建，训练和评估了GIA-NET。实验结果表明，所提出的GIA-NET在四个指标方面优于最先进的方法，包括测量感知相似性的深度指标。已经进行了广泛的消融研究，以通过利用全球信息来验证所提出的GIA-NET对低光成像的有效性。
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.
