Hofmann Julia, Goelzer Rilene, Wegner Daniel, Gladysz Szymon, Stein Karin
Appl Opt. 2021 Aug 1;60(22):F99-F108. doi: 10.1364/AO.425464.
Algorithms used for mitigation of the effects of atmospheric turbulence on video sequences often rely on a process for creating a reference image to register all of the frames. Because such a pristine image is generally not available, no-reference image quality metrics can be used to identify frames in a sequence that have minimum distortion. Here, we propose a metric that quantifies image warping by measuring image straightness based on line detection. The average length of straight lines in a frame is used to select best frames in a sequence and to generate a reference frame for a subsequent dewarping algorithm. We perform tests with this metric on simulated data that exhibits varying degrees of distortion and blur and spans normalized turbulence strengths between 0.75 and 4.5. We show, through these simulations, that the metric can differentiate between weak and moderate turbulence effects. We also show in simulations that the optical flow that uses a reference frame generated by this metric produces consistently improved image quality. This improvement is even higher when we employ the metric to guide optical flow that is applied to three real video sequences taken over a 7 km path.
用于减轻大气湍流对视频序列影响的算法通常依赖于创建参考图像以对齐所有帧的过程。由于通常无法获得这样的原始图像，因此可以使用无参考图像质量指标来识别序列中失真最小的帧。在此，我们提出一种指标，通过基于直线检测测量图像的直线度来量化图像扭曲。帧中直线的平均长度用于选择序列中的最佳帧，并为后续的去扭曲算法生成参考帧。我们使用该指标对模拟数据进行测试，这些模拟数据呈现出不同程度的失真和模糊，并且归一化湍流强度在0.75至4.5之间。通过这些模拟，我们表明该指标可以区分弱湍流效应和中等湍流效应。我们还在模拟中表明，使用由此指标生成的参考帧的光流能够持续提高图像质量。当我们使用该指标来指导应用于在7公里路径上拍摄的三个真实视频序列的光流时，这种改进甚至更高。
