Image restoration algorithms for atmospheric turbulence are known to be much more
challenging to design than traditional ones such as blur or noise because the distortion …

Fast and accurate simulation of imaging through atmospheric turbulence is essential for
developing turbulence mitigation algorithms. Recognizing the limitations of previous …

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 …

Whole-body biometric recognition is an important area of research due to its vast
applications in law enforcement, border security, and surveillance. This paper presents the …

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 …

For the simulation of anisoplanatic optical turbulence, split-step propagation is the gold
standard. Within the context of the degradations being limited to phase distortions, one …