GIA-Net: Global Information Aware Network for Low-light Imaging
This work addresses low-light imaging for computer vision applications, representing an incremental improvement by enhancing U-Nets with global information integration.
The paper tackles the problem of low-light imaging by proposing a global information aware (GIA) module to address artifacts like color inconsistency in vanilla U-Nets, resulting in a GIA-Net that outperforms state-of-the-art methods on multiple metrics including perceptual similarities.
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.