Ge Wei, Zhang Le, Zhan Weida, Wang Jiale, Zhu Depeng, Hong Yang
National Demonstration Center for Experimental Electrical, School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Entropy (Basel). 2023 Aug 12;25(8):1201. doi: 10.3390/e25081201.
Low-illumination image enhancement technology is a topic of interest in the field of image processing. However, while improving image brightness, it is difficult to effectively maintain the texture and details of the image, and the quality of the image cannot be guaranteed. In order to solve this problem, this paper proposed a low-illumination enhancement method based on structural and detail layers. Firstly, we designed an SRetinex-Net model. The network is mainly divided into two parts: a decomposition module and an enhancement module. Second, the decomposition module mainly adopts the SU-Net structure, which is an unsupervised network that decomposes the input image into a structural layer image and detail layer image. Afterward, the enhancement module mainly adopts the SDE-Net structure, which is divided into two branches: the SDE-S branch and the SDE-D branch. The SDE-S branch mainly enhances and adjusts the brightness of the structural layer image through Ehnet and Adnet to prevent insufficient or overexposed brightness enhancement in the image. The SDE-D branch is mainly denoised and enhanced with textural details through a denoising module. This network structure can greatly reduce computational costs. Moreover, we also improved the total variation optimization model as a mixed loss function and added structural metrics and textural metrics as variables on the basis of the original loss function, which can well separate the structure edge and texture edge. Numerous experiments have shown that our structure has a more significant impact on the brightness and detail preservation of image restoration.
低光照图像增强技术是图像处理领域的一个研究热点。然而，在提高图像亮度的同时，难以有效保持图像的纹理和细节，图像质量无法得到保证。为了解决这个问题，本文提出了一种基于结构层和细节层的低光照增强方法。首先，我们设计了一个SRetinex-Net模型。该网络主要分为两部分：分解模块和增强模块。其次，分解模块主要采用SU-Net结构，这是一个无监督网络，将输入图像分解为结构层图像和细节层图像。随后，增强模块主要采用SDE-Net结构，它分为两个分支：SDE-S分支和SDE-D分支。SDE-S分支主要通过Ehnet和Adnet增强和调整结构层图像的亮度，以防止图像亮度增强不足或过度曝光。SDE-D分支主要通过去噪模块对纹理细节进行去噪和增强。这种网络结构可以大大降低计算成本。此外，我们还改进了全变差优化模型作为混合损失函数，并在原损失函数的基础上增加了结构度量和纹理度量作为变量，能够很好地分离结构边缘和纹理边缘。大量实验表明，我们的结构对图像恢复的亮度和细节保留有更显著的影响。
