CNN-Based Restoration of a Single Face Image Degraded by Atmospheric Turbulence
CNN-Based Restoration of a Single Face Image Degraded by Atmospheric Turbulence
复制标题
DOI：
10.1109/tbiom.2022.3169697
复制
发表时间：
2022-04-01
期刊：
影响因子：
--
通讯作者：
Patel, Vishal M.
中科院分区：
文献类型：
--
作者：
Yasarla, Rajeev;Patel, Vishal M.
大气湍流显著地影响使用已经通过长大气路径传播的光的成像系统。在这种条件下捕获的图像遭受几何变形和模糊的组合。我们提出了一种基于深度学习的解决方案来恢复单个连续性退化的人脸图像，其中每个像素位置的几何失真和模糊量首先使用两个单独的网络根据方差图进行估计。然后，湍流失真消除网络（TDRN）使用估计的方差图来恢复图像。此外，提出了一种基于置信度引导的图像梯度损失训练TDRN。对合成和真实的人脸图像进行的综合实验表明，该框架能够有效缓解大气湍流引起的模糊和几何失真，显著提高视觉质量。此外，烧蚀研究，以证明所提出的方法中的不同模块所获得的改进。
Atmospheric turbulence significantly affects imaging systems which use light that has propagated through long atmospheric paths. Images captured under such condition suffer from a combination of geometric deformation and blur. We present a deep learning-based solution to the problem of restoring a single turbulence-degraded face image where the amount of geometric distortion and blur at each pixel location is first estimated in terms of variance maps using two separate networks. The estimated variance maps are then used by the Turbulence Distortion Removal Network (TDRN) to restore the image. Furthermore, a confidence-guided image gradient-based loss is proposed to train TDRN. Comprehensive experiments on synthetic and real face images show that the proposed framework is capable of alleviating blur and geometric distortion caused by atmospheric turbulence, and can significantly improve the visual quality. In addition, an ablation study is performed to demonstrate the improvements obtained by different modules in the proposed method.