论文标题
大气湍流下的热到可见图像合成
Thermal to Visible Image Synthesis under Atmospheric Turbulence
论文作者
论文摘要
在许多实际应用中，诸如生物识别和监视之类的远程成像，热想模式通常用于捕获在弱光和夜间条件下的图像。但是，这种成像系统通常会遭受大气湍流的影响，这会引入严重的模糊和变形伪像于捕获的图像。在远程成像中，这种问题是不可避免的，并显着降低了面部验证精度。在本文中，我们首先通过现实世界中热图像上的湍流模拟方法研究了问题。然后提出了一种端到端的重建方法，该方法可以通过基于预先训练的stylegan2网络利用自然图像先验来将热图像直接转换为可见的光谱图像。与现有的连续湍流降低和热图像转换的两步方法相比，我们的方法被证明在重建结果的视觉质量和面部验证准确性方面都有效。此外，据我们所知，这是研究大气湍流下热图像转换到可见图像翻译问题的第一部作品。
In many practical applications of long-range imaging such as biometrics and surveillance, thermal imagining modalities are often used to capture images in low-light and nighttime conditions. However, such imaging systems often suffer from atmospheric turbulence, which introduces severe blur and deformation artifacts to the captured images. Such an issue is unavoidable in long-range imaging and significantly decreases the face verification accuracy. In this paper, we first investigate the problem with a turbulence simulation method on real-world thermal images. An end-to-end reconstruction method is then proposed which can directly transform thermal images into visible-spectrum images by utilizing natural image priors based on a pre-trained StyleGAN2 network. Compared with the existing two-steps methods of consecutive turbulence mitigation and thermal to visible image translation, our method is demonstrated to be effective in terms of both the visual quality of the reconstructed results and face verification accuracy. Moreover, to the best of our knowledge, this is the first work that studies the problem of thermal to visible image translation under atmospheric turbulence.
