论文标题
学习使用不确定性恢复大气湍流降解的图像
Learning to restore images degraded by atmospheric turbulence using uncertainty
论文作者
论文摘要
大气湍流可以通过在大气折射指数中引起空间和时间随机的波动，从而显着降低远程成像系统获得的图像质量。折射率的变化导致捕获的图像几何扭曲和模糊。因此，重要的是要补偿由大气湍流引起的图像中的视觉降解。在本文中，我们提出了一种基于大气湍流降低的单个图像的深度学习方法。我们利用基于蒙特卡洛辍学的认知不确定性来捕获网络很难恢复的图像中的区域。然后，使用估计的不确定性图来指导网络以获得还原图像。对合成图像和真实图像进行了广泛的实验，以显示拟议工作的重要性。代码可在以下网址找到：https：//github.com/rajeevyasarla/at-net
Atmospheric turbulence can significantly degrade the quality of images acquired by long-range imaging systems by causing spatially and temporally random fluctuations in the index of refraction of the atmosphere. Variations in the refractive index causes the captured images to be geometrically distorted and blurry. Hence, it is important to compensate for the visual degradation in images caused by atmospheric turbulence. In this paper, we propose a deep learning-based approach for restring a single image degraded by atmospheric turbulence. We make use of the epistemic uncertainty based on Monte Carlo dropouts to capture regions in the image where the network is having hard time restoring. The estimated uncertainty maps are then used to guide the network to obtain the restored image. Extensive experiments are conducted on synthetic and real images to show the significance of the proposed work. Code is available at : https://github.com/rajeevyasarla/AT-Net
