基于生成式对抗网络的太赫兹图像增强

太赫兹扫描成像中,由于激光器功率波动和仪器振动等原因,导致图像对比度较低,成像质量有待提高,且目前针对太赫兹图像的处理还停留在传统算法阶段.本文结合深度学习思想,提出了一种基于生成式对抗网络的图像增强方法.通过对训练集图像引入模糊和噪声,学习低质量图像和高质量图像之间的映射关系,并将其应用在真实太赫兹图像中.实验结果表...

Terahertz Image Enhancement Based on Generative Adversarial Network

In terahertz scanning imaging,the image contrast is low due to laser power fluctuation and instrument vibration,and the imaging quality needs to be improved.At present,the processing of terahertz image is still in the traditional algorithm stage.In this paper,an image enhancement method based on Generative Adversarial Network is proposed,which includes the idea of deep learning.By introducing blur and noise into the training set image,the mapping relationship between low-quality images and high-quality images is learned and applied to real terahertz images.The experimental results show that,compared with traditional algorithms such as bilateral filtering and non-local mean filtering,this method can significantly improve the image contrast on the basis of improving image details,and has a good visual sense,which provides a new idea for terahertz image enhancement.