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.

本文介绍了一种在低亮度条件下进行图像增强的轻量级神经网络，提出了全局低光强度增强和局部自适应校正的方法来解决不同图像区域的曝光问题，实验结果表明该方法在客观指标和主观视觉效果方面都表现出色。