A variety of neural networks architectures are being studied to tackle blur in images and videos caused by a non-steady camera and objects being captured. In this paper, we present an overview of these existing networks and perform experiments to remove the blur caused by atmospheric turbulence. Our experiments aim to examine the reusability of existing networks and identify desirable aspects of the architecture in a system that is geared specifically towards atmospheric turbulence mitigation. We compare five different architectures, including a network trained in an end-to-end fashion, thereby removing the need for a stabilization step.

翻译：针对由非稳定相机及拍摄物体运动引起的图像与视频模糊问题，各类神经网络架构正被广泛研究。本文系统综述了现有网络模型，并通过实验验证其在消除大气湍流所致模糊方面的性能。本实验旨在考察现有网络的可复用性，并针对专门面向大气湍流抑制的系统，识别网络架构中具有优势的设计特性。我们比较了五种不同架构，包括采用端到端训练方式的网络，该方式无需额外的图像稳定预处理步骤。