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Found 9 papers, 2 papers with code
Spatio-Temporal Turbulence Mitigation: A Translational Perspective
Building upon the intuitions of classical TM algorithms, we present the Deep Atmospheric TUrbulence Mitigation network (DATUM).
FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude
Whole-body biometric recognition is an important area of research due to its vast applications in law enforcement, border security, and surveillance.
Scattering and Gathering for Spatially Varying Blurs
A spatially varying blur kernel $h(\mathbf{x},\mathbf{u})$ is specified by an input coordinate $\mathbf{u} \in \mathbb{R}^2$ and an output coordinate $\mathbf{x} \in \mathbb{R}^2$.
Real-Time Dense Field Phase-to-Space Simulation of Imaging through Atmospheric Turbulence
We show that the cross-correlation of the Zernike modes has an insignificant contribution to the statistics of the random samples.
Imaging through the Atmosphere using Turbulence Mitigation Transformer
While existing deep-learning-based methods have demonstrated promising results in specific testing conditions, they suffer from three limitations: (1) lack of generalization capability from synthetic training data to real turbulence data; (2) failure to scale, hence causing memory and speed challenges when extending the idea to a large number of frames; (3) lack of a fast and accurate simulator to generate data for training neural networks.
Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transform
Fast and accurate simulation of imaging through atmospheric turbulence is essential for developing turbulence mitigation algorithms.
Image Reconstruction of Static and Dynamic Scenes through Anisoplanatic Turbulence
Ground based long-range passive imaging systems often suffer from degraded image quality due to a turbulent atmosphere.
Simulating Anisoplanatic Turbulence by Sampling Inter-modal and Spatially Correlated Zernike Coefficients
Simulating atmospheric turbulence is an essential task for evaluating turbulence mitigation algorithms and training learning-based methods.
Rethinking Atmospheric Turbulence Mitigation
We demonstrate how a simple prior can outperform state-of-the-art blind deconvolution methods.
