Presentation + Paper
8 June 2022 Evaluation of neural network algorithms for atmospheric turbulence mitigation
Author Affiliations +
Abstract
A variety of neural networks architectures are being studied to tackle blur in images and videos caused by a non-steady camera and objects being captured. In this paper, we present an overview of these existing networks and perform experiments to remove the blur caused by atmospheric turbulence. Our experiments aim to examine the reusability of existing networks and identify desirable aspects of the architecture in a system that is geared specifically towards atmospheric turbulence mitigation. We compare five different architectures, including a network trained in an end-to-end fashion, thereby removing the need for a stabilization step.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tushar Jain, Madeline Lubien, and Jérôme Gilles "Evaluation of neural network algorithms for atmospheric turbulence mitigation", Proc. SPIE 12122, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI, 121220T (8 June 2022); https://doi.org/10.1117/12.2614567
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Neural networks

Turbulence

Atmospheric turbulence

Deconvolution

Computer simulations

Evolutionary algorithms

Image processing

Back to Top