Learning a Single Convolutional Layer Model for Low Light Image
  Enhancement
Yuantong Zhang Baoxin Teng Daiqin Yang Zhenzhong Chen Haichuan Ma Gang Li Wenpeng Ding
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.