Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details
This addresses visibility issues in applications like surveillance and remote sensing, but it is incremental as it builds on existing low-light enhancement techniques.
The paper tackles the problem of low-light image enhancement by simultaneously improving brightness and preserving details, achieving superior performance over state-of-the-art methods on benchmark datasets.
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.