Image distortion by atmospheric turbulence is a stochastic degradation, which is a critical
problem in long-range optical imaging systems. A number of research has been conducted …

Numerical simulation of atmospheric turbulence is one of the biggest bottlenecks in
developing computational techniques for solving the inverse problem in long-range imaging …

Imaging at a long distance often requires advanced image restoration algorithms to
compensate for the distortions caused by atmospheric turbulence. However, unlike many …

Since the seminal work of Andrey Kolmogorov in the early 1940's, imaging through
atmospheric turbulence has grown from a pure scientific pursuit to an important subject …

Recovering images distorted by atmospheric turbulence is a challenging inverse problem
due to the stochastic nature of turbulence. Although numerous turbulence mitigation (TM) …

The atmospheric turbulence mitigation problem has emerged as a challenging inverse
problem in the communities of computer vision and optics. However, current methods either …

A spatially varying blur kernel is specified by an input coordinate and an output coordinate.
For computational efficiency, we sometimes write as a linear combination of spatially …

Face and person recognition have recently achieved remarkable success under challenging
scenarios, such as off-pose and cross-spectrum matching. However, long-range recognition …

The removal of atmospheric turbulence is crucial for long-distance imaging. Leveraging the
stochastic nature of atmospheric turbulence numerous algorithms have been developed that …

The influence of atmospheric turbulence on acquired imagery makes image interpretation
and scene analysis extremely difficult and reduces the effectiveness of conventional …