School of Automobile, Chang'an University, Xi'an, Shaanxi 710064, China.
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.
Comput Intell Neurosci. 2022 Aug 20;2022:4942420. doi: 10.1155/2022/4942420. eCollection 2022.
Low-light image enhancement is a preprocessing work for many recognition and tracking tasks for autonomous driving at night. It needs to handle various factors simultaneously including uneven lighting, low contrast, and artifacts. We propose a novel end-to-end Retinex-based illumination attention low-light enhancement network. Specifically, our proposed method adopts multibranch architecture to extract rich features for different depth levels. Meanwhile, we consider the features from different scales in built-in illumination attention module. We encode reflectance features and illumination features into latent space based on Retinex in each submodule, which could cater for highly ill-posed image decomposition tasks. It aims to enhance the desired illumination features under different receptive fields. Subsequently, we propose a memory gate mechanism to learn adaptively long-term and short-term memory. Their weight could control how many high-level and low-level features should be reserved. This method could improve the image quality from both different feature scales and feature levels. Comprehensive experiments on BDD10K and cityscapes datasets demonstrate that our proposed method outperforms various types of methods in terms of visual quality and quantitative metrics. We also show that our proposed method has certain antinoise capability and generalizes well without fine-tuning when dealing with unseen images. Meanwhile, our restoration performance is comparable to that of advanced computationally intensive models..
弱光图像增强是夜间自动驾驶中许多识别和跟踪任务的预处理工作。它需要同时处理各种因素，包括光照不均匀、对比度低和伪影。我们提出了一种新颖的基于 Retinex 的照明注意弱光增强网络。具体来说，我们提出的方法采用多分支架构从不同深度水平提取丰富的特征。同时，我们在内置照明注意模块中考虑了来自不同尺度的特征。我们在每个子模块中基于 Retinex 将反射率特征和照度特征编码到潜在空间中，这可以适应高度不适定的图像分解任务。它旨在增强不同感受野下所需的光照特征。随后，我们提出了一种记忆门机制来学习自适应的长期和短期记忆。它们的权重可以控制应该保留多少高层和低层特征。该方法可以从不同的特征尺度和特征层次提高图像质量。在 BDD10K 和 cityscapes 数据集上的综合实验表明，我们提出的方法在视觉质量和定量指标方面优于各种类型的方法。我们还表明，我们提出的方法在处理看不见的图像时具有一定的抗噪能力，并且无需微调即可很好地泛化。同时，我们的恢复性能可与先进的计算密集型模型相媲美。
