Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GIA-Net: Global Information Aware Network for Low-light Imaging   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.

22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom