Low-light image enhancement (LLIE) aims to improve the illuminance of images due to insufficient light exposure. Recently, various lightweight learning-based LLIE methods have been proposed to handle the challenges of unfavorable prevailing low contrast, low brightness, etc. In this paper, we have streamlined the architecture of the network to the utmost degree. By utilizing the effective structural re-parameterization technique, a single convolutional layer model (SCLM) is proposed that provides global low-light enhancement as the coarsely enhanced results. In addition, we introduce a local adaptation module that learns a set of shared parameters to accomplish local illumination correction to address the issue of varied exposure levels in different image regions. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art LLIE methods in both objective metrics and subjective visual effects. Additionally, our method has fewer parameters and lower inference complexity compared to other learning-based schemes.

翻译：低光图像增强旨在改善因光照不足导致的图像照度。近年来，各种轻量级基于学习的低光图像增强方法被提出，以应对普遍存在的低对比度、低亮度等不利挑战。本文中，我们将网络架构简化至极致。通过利用有效的结构重参数化技术，提出了一种单一卷积层模型，该模型提供全局低光增强作为粗略增强结果。此外，我们引入了一个局部自适应模块，学习一组共享参数以完成局部光照校正，从而解决不同图像区域曝光水平不一的问题。实验结果表明，所提方法在客观指标和主观视觉效果上均优于现有最先进的低光图像增强方法。此外，与其它基于学习的方案相比，我们的方法参数更少且推理复杂度更低。