一种基于U-Net生成对抗网络的低照度图像增强方法

在低照度环境下采集的图像具有低信噪比、低对比度及低分辨率等特点，导致图像难以识别利用.为了提升低照度图像的质量，本文提出一种基于U-Net生成对抗网络的低照度图像增强方法.首先利用U-Net框架实现生成对抗网络中的生成网络，然后利用该生成对抗网络学习从低照度图像到正常照度图像的特征映射，最终实现低照度图像的照度增强.实验结果表明，与主流算法相比，本文提出的方法能够更有效的提升低照度图像的亮度与对比度.

Low-Light Image Enhancement Method Based on U-Net Generative Adversarial Network

The images acquired in the low illumination environment have the characteristics of low signal-to-noise ratio, low contrast and low resolution, which make the image difficult to identify and utilize.In order to improve the image quality of low-light images, this paper proposes a low-light image enhancement method based on U-net generative adversarial network (GAN).First, the U-net framework is used to implement the generative network of GAN, and then the GAN is used to learn the feature mapping from the low-light image to the normal-light image, and ultimately achieve illumination enhancement for the low-light image.Finally, this method is verified by experiments.The experimental results show that, compared with the mainstream algorithm, the proposed algorithm can effectively improve the brightness and contrast of low-light image.