TY - GEN
T1 - Atmospheric turbulence mitigation based on turbulence extraction
AU - He, Renjie
AU - Wang, Zhiyong
AU - Fan, Yangyu
AU - Fengg, David
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/5/18
Y1 - 2016/5/18
N2 - 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.
AB - 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.
KW - atmospheric turbulence
KW - guided filter
KW - image restoration
KW - low-rank decomposition
UR - http://www.scopus.com/inward/record.url?scp=84973316024&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2016.7471915
DO - 10.1109/ICASSP.2016.7471915
M3 - 会议稿件
AN - SCOPUS:84973316024
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1442
EP - 1446
BT - 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Y2 - 20 March 2016 through 25 March 2016
ER -