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.
© The Authors
Society of Photo-optical Instrumentation Engineers
atmospheric turbulence, convolutional neural network, deep learning, turbulence mitigation, University of Dayton Electro-optics and Photonics
Air Force Research Laboratory, FA8650-18-F-1710
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.