Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details

TL;DR

This paper introduces NEID, a two-stream deep learning framework that enhances low-light images by simultaneously improving brightness and details, outperforming existing methods with efficient computation.

Contribution

The novel NEID framework effectively combines light enhancement and detail refinement using a multi-task feature fusion approach with channel attention.

Findings

Outperforms state-of-the-art low-light enhancement methods

Improves visibility and detail preservation in benchmark datasets

Operates with low computational costs

Abstract

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.