Document Type
Article
Publication Date
3-1-2021
Publication Source
Optical Engineering
Abstract
We present a deep learning approach for restoring images degraded by atmospheric optical turbulence. We consider the case of terrestrial imaging over long ranges with a wide field-of-view. This produces an anisoplanatic imaging scenario where turbulence warping and blurring vary spatially across the image. The proposed turbulence mitigation (TM) method assumes that a sequence of short-exposure images is acquired. A block matching (BM) registration algorithm is applied to the observed frames for dewarping, and the resulting images are averaged. A convolutional neural network (CNN) is then employed to perform spatially adaptive restoration. We refer to the proposed TM algorithm as the block matching and CNN (BM-CNN) method. Training the CNN is accomplished using simulated data from a fast turbulence simulation tool capable of producing a large amount of degraded imagery from declared truth images rapidly. Testing is done using independent data simulated with a different well-validated numerical wave-propagation simulator. Our proposed BM-CNN TM method is evaluated in a number of experiments using quantitative metrics. The quantitative analysis is made possible by virtue of having truth imagery from the simulations. A number of restored images are provided for subjective evaluation. We demonstrate that the BM-CNN TM method outperforms the benchmark methods in the scenarios tested.
ISBN/ISSN
0091-3286
Document Version
Published Version
Copyright
© The Authors
Publisher
Society of Photo-optical Instrumentation Engineers
Volume
60
Peer Reviewed
yes
Issue
3
Keywords
atmospheric turbulence, convolutional neural network, deep learning, turbulence mitigation, University of Dayton Electro-optics and Photonics
Sponsoring Agency
Air Force Research Laboratory, FA8650-18-F-1710
eCommons Citation
Hoffmire, Matthew A.; Hardie, Russell C.; Rucci, Michael A.; Van Hook, Richard; and Karch, Barry K., "Deep learning for anisoplanatic optical turbulence mitigation in long-range imaging" (2021). Electrical and Computer Engineering Faculty Publications. 422.
https://ecommons.udayton.edu/ece_fac_pub/422
Comments
Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI: https://doi.org/10.1117/1.OE.60.3.033103