  The captured images under low light conditions often suffer insufficient
brightness and notorious noise. Hence, low-light image enhancement is a key
challenging task in computer vision. A variety of methods have been proposed
for this task, but these methods often failed in an extreme low-light
environment and amplified the underlying noise in the input image. To address
such a difficult problem, this paper presents a novel attention-based neural
network to generate high-quality enhanced low-light images from the raw sensor
data. Specifically, we first employ attention strategy (i.e. channel attention
and spatial attention modules) to suppress undesired chromatic aberration and
noise. The channel attention module guides the network to refine redundant
colour features. The spatial attention module focuses on denoising by taking
advantage of the non-local correlation in the image. Furthermore, we propose a
new pooling layer, called inverted shuffle layer, which adaptively selects
useful information from previous features. Extensive experiments demonstrate
the superiority of the proposed network in terms of suppressing the chromatic
aberration and noise artifacts in enhancement, especially when the low-light
image has severe noise.
