Wen Chaoran, Nie Ting, Li Mingxuan, Wang Xiaofeng, Huang Liang
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2023 Oct 13;23(20):8442. doi: 10.3390/s23208442.
Under low-illumination conditions, the quality of the images collected by the sensor is significantly impacted, and the images have visual problems such as noise, artifacts, and brightness reduction. Therefore, this paper proposes an effective network based on Retinex for low-illumination image enhancement. Inspired by Retinex theory, images are decomposed into two parts in the decomposition network, and sent to the sub-network for processing. The reconstruction network constructs global and local residual convolution blocks to denoize the reflection component. The enhancement network uses frequency information, combined with attention mechanism and residual density network to enhance contrast and improve the details of the illumination component. A large number of experiments on public datasets show that our method is superior to existing methods in both quantitative and visual aspects.
在低光照条件下，传感器采集的图像质量会受到显著影响，图像会出现噪声、伪像和亮度降低等视觉问题。因此，本文提出了一种基于Retinex的有效网络用于低光照图像增强。受Retinex理论启发，在分解网络中将图像分解为两部分，并将其发送到子网络进行处理。重建网络构建全局和局部残差卷积块对反射分量进行去噪。增强网络利用频率信息，结合注意力机制和残差密度网络来增强对比度并改善光照分量的细节。在公共数据集上进行的大量实验表明，我们的方法在定量和视觉方面均优于现有方法。
