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Found 11 papers, 5 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.
Structured Kernel Estimation for Photon-Limited Deconvolution
By modeling the blur kernel using a low-dimensional representation with the key points on the motion trajectory, we significantly reduce the search space and improve the regularity of the kernel estimation problem.
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.
Photon-Limited Blind Deconvolution using Unsupervised Iterative Kernel Estimation
When the noise is strong, these networks fail to simultaneously deblur and denoise; (3) While iterative schemes are known to be robust in the classical frameworks, they are seldom considered in deep neural networks because it requires a differentiable non-blind solver.
Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and A New Physics-Inspired Transformer Model
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 caused by the turbulence is an entanglement of spatially varying blur, geometric distortion, and sensor noise.
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.
Student-Teacher Learning from Clean Inputs to Noisy Inputs
Feature-based student-teacher learning, a training method that encourages the student's hidden features to mimic those of the teacher network, is empirically successful in transferring the knowledge from a pre-trained teacher network to the student network.
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.
Rethinking Atmospheric Turbulence Mitigation
We demonstrate how a simple prior can outperform state-of-the-art blind deconvolution methods.
