  With the goal of tuning up the brightness, low-light image enhancement enjoys
numerous applications, such as surveillance, remote sensing and computational
photography. Images captured under low-light conditions often suffer from poor
visibility and blur. Solely brightening the dark regions will inevitably
amplify the blur, thus may lead to detail loss. In this paper, we propose a
simple yet effective two-stream framework named NEID to tune up the brightness
and enhance the details simultaneously without introducing many computational
costs. Precisely, the proposed method consists of three parts: Light
Enhancement (LE), Detail Refinement (DR) and Feature Fusing (FF) module, which
can aggregate composite features oriented to multiple tasks based on channel
attention mechanism. Extensive experiments conducted on several benchmark
datasets demonstrate the efficacy of our method and its superiority over
state-of-the-art methods.
