Deep learning for anisoplanatic optical turbulence mitigation in long-range imaging
Autor: | Barry K. Karch, Russell C. Hardie, Michael A. Rucci, Richard Van Hook, Matthew A. Hoffmire |
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Rok vydání: | 2021 |
Předmět: |
Image fusion
Matching (graph theory) business.industry Computer science Deep learning General Engineering Image registration Image processing 02 engineering and technology 01 natural sciences Convolutional neural network Atomic and Molecular Physics and Optics 010309 optics 020210 optoelectronics & photonics 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Computer vision Artificial intelligence Image warping business Block (data storage) |
Zdroj: | Optical Engineering. 60 |
ISSN: | 0091-3286 |
Popis: | 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. |
Databáze: | OpenAIRE |
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