Deep learning for anisoplanatic optical turbulence mitigation in long-range imaging
نویسندگان
چکیده
We present a deep learning approach for restoring images degraded by atmospheric optical turbulence. consider the case of terrestrial imaging over long ranges with wide field-of-view. This produces an anisoplanatic scenario where turbulence warping and blurring vary spatially across image. The proposed mitigation (TM) method assumes that sequence short-exposure is acquired. A block matching (BM) registration algorithm applied to observed frames dewarping, resulting are averaged. convolutional neural network (CNN) then employed perform adaptive restoration. refer TM as CNN (BM-CNN) method. Training accomplished using simulated data from fast simulation tool capable producing large amount imagery declared truth rapidly. Testing done independent different well-validated numerical wave-propagation simulator. Our BM-CNN evaluated in number experiments quantitative metrics. analysis made possible virtue having simulations. restored provided subjective evaluation. demonstrate outperforms benchmark methods scenarios tested.
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ژورنال
عنوان ژورنال: Optical Engineering
سال: 2021
ISSN: ['1560-2303', '0091-3286']
DOI: https://doi.org/10.1117/1.oe.60.3.033103