论文标题
学会使用CNN恢复大气湍流降解的单个面部图像
Learning to Restore a Single Face Image Degraded by Atmospheric Turbulence using CNNs
论文作者
论文摘要
大气湍流显着影响成像系统，这些成像系统使用了通过长大气路径传播的光。在这种情况下捕获的图像遭受了几何变形和空间变化的模糊的组合。我们为恢复湍流衰减的面部图像的问题提供了一种基于学习的解决方案，其中首先使用两个单独的网络估算了面部图像的几何变形和模糊的先前信息。然后，估计的先验信息由称为湍流失真去除网络（TDRN）的网络使用，以纠正几何变形并减少面部图像中的模糊。此外，提出了一种新的损失来训练TDRN，其中计算第一和二阶图像梯度及其置信图以减轻湍流降解的影响。关于合成和真实面部图像的综合实验表明，该框架能够减轻由大气湍流引起的模糊和几何变形，并显着提高视觉质量。此外，进行消融研究以证明所提出方法中不同模块获得的改进。
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 space varying blur. We present a deep learning-based solution to the problem of restoring a turbulence-degraded face image where prior information regarding the amount of geometric distortion and blur at each location of the face image is first estimated using two separate networks. The estimated prior information is then used by a network called, Turbulence Distortion Removal Network (TDRN), to correct geometric distortion and reduce blur in the face image. Furthermore, a novel loss is proposed to train TDRN where first and second order image gradients are computed along with their confidence maps to mitigate the effect of turbulence degradation. Comprehensive experiments on synthetic and real face images show that this framework is capable of alleviating blur and geometric distortion caused by atmospheric turbulence, and significantly improves the visual quality. In addition, an ablation study is performed to demonstrate the improvements obtained by different modules in the proposed method.
