论文标题
Deanet：低光图像增强的分解增强和调整网络
DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement
论文作者
论文摘要
在弱光条件下获得的图像将严重影响图像的质量。解决较差的弱光图像质量的问题可以有效地提高图像的视觉质量，并更好地提高计算机视觉的可用性。此外，它在许多领域都具有非常重要的应用。本文提出了基于视网膜的Deanet，以增强弱光图像。它将图像的频率信息和内容信息结合到三个子网络中：分解网络，增强网络和调整网络。这三个子网络分别用于分解，降解，对比度增强和细节保存，调整和图像产生。我们的模型对于所有弱光图像都具有良好的强大结果。该模型对公共数据集进行了培训，并且实验结果表明，就视力和质量而言，我们的方法比现有的最新方法更好。
Images obtained under low-light conditions will seriously affect the quality of the images. Solving the problem of poor low-light image quality can effectively improve the visual quality of images and better improve the usability of computer vision. In addition, it has very important applications in many fields. This paper proposes a DEANet based on Retinex for low-light image enhancement. It combines the frequency information and content information of the image into three sub-networks: decomposition network, enhancement network and adjustment network. These three sub-networks are respectively used for decomposition, denoising, contrast enhancement and detail preservation, adjustment, and image generation. Our model has good robust results for all low-light images. The model is trained on the public data set LOL, and the experimental results show that our method is better than the existing state-of-the-art methods in terms of vision and quality.
