Wang Hua, Cao Jianzhong, Huang Jijiang
Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Entropy (Basel). 2024 Feb 22;26(3):184. doi: 10.3390/e26030184.
Low-light image enhancement (LLIE) aims to improve the visual quality of images taken under complex low-light conditions. Recent works focus on carefully designing Retinex-based methods or end-to-end networks based on deep learning for LLIE. However, these works usually utilize pixel-level error functions to optimize models and have difficulty effectively modeling the real visual errors between the enhanced images and the normally exposed images. In this paper, we propose an adaptive dual aggregation network with normalizing flows (ADANF) for LLIE. First, an adaptive dual aggregation encoder is built to fully explore the global properties and local details of the low-light images for extracting illumination-robust features. Next, a reversible normalizing flow decoder is utilized to model real visual errors between enhanced and normally exposed images by mapping images into underlying data distributions. Finally, to further improve the quality of the enhanced images, a gated multi-scale information transmitting module is leveraged to introduce the multi-scale information from the adaptive dual aggregation encoder into the normalizing flow decoder. Extensive experiments on paired and unpaired datasets have verified the effectiveness of the proposed ADANF.
低光图像增强（LLIE）旨在提高在复杂低光条件下拍摄图像的视觉质量。近期的工作主要集中在精心设计基于视网膜模型的方法或基于深度学习的端到端网络用于低光图像增强。然而，这些工作通常利用像素级误差函数来优化模型，并且难以有效地对增强图像和正常曝光图像之间的真实视觉误差进行建模。在本文中，我们提出了一种用于低光图像增强的具有归一化流的自适应双聚合网络（ADANF）。首先，构建一个自适应双聚合编码器，以充分探索低光图像的全局特性和局部细节，用于提取光照鲁棒特征。接下来，利用一个可逆归一化流解码器，通过将图像映射到潜在数据分布来对增强图像和正常曝光图像之间的真实视觉误差进行建模。最后，为了进一步提高增强图像的质量，利用一个门控多尺度信息传输模块将来自自适应双聚合编码器的多尺度信息引入到归一化流解码器中。在配对和非配对数据集上进行的大量实验验证了所提出的ADANF的有效性。
