Atmospheric turbulence mitigation based on turbulence extraction
Renjie He, Zhiyong Wang, Yangyu Fan, David D. Feng
Abstract
A video taken under the influence of atmospheric turbulence suffers from serious distortion caused by the variation of optical refractive index. In order to reduce geometric distortion and time-space-varying blur, and recover both coarse structure and fine details, a novel turbulence extraction based approach for recovering a latent image from an atmospheric turbulence degraded imagery sequence is proposed. Firstly, a non-rigid image registration method is applied as a preprocessing to reduce geometric deformation. Secondly, the registered image sequence is decomposed into a low-rank background scene component and a sparse turbulent component via matrix decomposition. Different from other approaches, which intend to remove turbulence directly, we manage to extract information of distortion position from the sparse turbulent component to indicate the sharpest turbulence patches. The selected sharpest turbulence patches are then enhanced and fused to generate an enhanced detail layer. Finally, the output image is generated by fusing the deblurred background scene layer and the enhanced detail layer together. Experiments indicate that our approach is capable of significantly alleviating atmospheric turbulence blur and geometric distortion.
BibTeX
@inproceedings{icassp2016_atmosphericturbu,
title = {Atmospheric turbulence mitigation based on turbulence extraction},
author = {Renjie He and Zhiyong Wang and Yangyu Fan and David D. Feng},
booktitle = {ICASSP 2016},
year = {2016}
}