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It is extremely challenging to acquire perceptually plausible images underlow-light conditions due to low SNR. Most recently, U-Nets have shown promisingresults for low-light imaging. However, vanilla U-Nets generate images withartifacts such as color inconsistency due to the lack of global colorinformation. In this paper, we propose a global information aware (GIA) module,which is capable of extracting and integrating the global information into thenetwork to improve the performance of low-light imaging. The GIA module can beinserted into a vanilla U-Net with negligible extra learnable parameters orcomputational cost. Moreover, a GIA-Net is constructed, trained and evaluatedon a large scale real-world low-light imaging dataset. Experimental resultsshow that the proposed GIA-Net outperforms the state-of-the-art methods interms of four metrics, including deep metrics that measure perceptualsimilarities. Extensive ablation studies have been conducted to verify theeffectiveness of the proposed GIA-Net for low-light imaging by utilizing globalinformation.

GIA-Net: Global Information Aware Network for Low-light Imaging