论文标题
无监督的低光图像增强与退联网络的增强
Unsupervised Low-light Image Enhancement with Decoupled Networks
论文作者
论文摘要
在本文中，我们解决了增强现实世界中低光图像的问题，并以无监督的方式噪音。常规的无监督学习方法通常使用图像到图像翻译模型解决低光图像增强问题。它们主要集中于照明或对比度增强，但无法抑制在现实世界中低光条件下拍摄的图像中无处不在的噪声。为了解决这个问题，我们将此任务明确地分解为两个子任务：照明增强和抑制噪声。我们建议学习一个两阶段的基于GAN的框架，以完全无监督的方式增强现实世界中的低光图像。为了促进对模型的无监督培训，我们使用伪标签构建样品。此外，我们提出一种自适应内容损失，以根据照明强度抑制不同区域中的真实图像噪声。除了传统的基准数据集外，还构建并用于彻底评估模型的性能。广泛的实验表明，我们所提出的方法优于最新的无监督图像增强方法，从照明增强和降低降噪方面。
In this paper, we tackle the problem of enhancing real-world low-light images with significant noise in an unsupervised fashion. Conventional unsupervised learning-based approaches usually tackle the low-light image enhancement problem using an image-to-image translation model. They focus primarily on illumination or contrast enhancement but fail to suppress the noise that ubiquitously exists in images taken under real-world low-light conditions. To address this issue, we explicitly decouple this task into two sub-tasks: illumination enhancement and noise suppression. We propose to learn a two-stage GAN-based framework to enhance the real-world low-light images in a fully unsupervised fashion. To facilitate the unsupervised training of our model, we construct samples with pseudo labels. Furthermore, we propose an adaptive content loss to suppress real image noise in different regions based on illumination intensity. In addition to conventional benchmark datasets, a new unpaired low-light image enhancement dataset is built and used to thoroughly evaluate the performance of our model. Extensive experiments show that our proposed method outperforms the state-of-the-art unsupervised image enhancement methods in terms of both illumination enhancement and noise reduction.
