Tang Linfeng, Ma Jiayi, Zhang Hao, Guo Xiaojie
IEEE Trans Neural Netw Learn Syst. 2024 Feb;35(2):2694-2707. doi: 10.1109/TNNLS.2022.3190880. Epub 2024 Feb 5.
Low-light image enhancement (LIME) aims to convert images with unsatisfied lighting into desired ones. Different from existing methods that manipulate illumination in uncontrollable manners, we propose a flexible framework to take user-specified guide images as references to improve the practicability. To achieve the goal, this article models an image as the combination of two components, that is, content and exposure attribute, from an information decoupling perspective. Specifically, we first adopt a content encoder and an attribute encoder to disentangle the two components. Then, we combine the scene content information of the low-light image with the exposure attribute of the guide image to reconstruct the enhanced image through a generator. Extensive experiments on public datasets demonstrate the superiority of our approach over state-of-the-art alternatives. Particularly, the proposed method allows users to enhance images according to their preferences, by providing specific guide images. Our source code and the pretrained model are available at https://github.com/Linfeng-Tang/DRLIE.
低光照图像增强（LIME）旨在将光照不理想的图像转换为理想的图像。与现有以不可控方式处理光照的方法不同，我们提出了一个灵活的框架，以用户指定的引导图像作为参考来提高实用性。为实现这一目标，本文从信息解耦的角度将图像建模为两个组件的组合，即内容和曝光属性。具体而言，我们首先采用内容编码器和属性编码器来分离这两个组件。然后，我们将低光照图像的场景内容信息与引导图像的曝光属性相结合，通过生成器重建增强后的图像。在公共数据集上进行的大量实验证明了我们的方法优于现有最先进的方法。特别地，所提出的方法允许用户通过提供特定的引导图像，根据自己的偏好增强图像。我们的源代码和预训练模型可在https://github.com/Linfeng-Tang/DRLIE获取。
