Straightness metric for measurement of turbulence-induced distortion in long-range imaging

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.