An Adaptive Image Registration Technique to Remove Atmospheric Turbulence

  • Akshay Patel Post Graduate Student
  • Dippal Israni Assistant Professor
  • Nerella Arun Mani Kumar Member (Research Staff)
  • Chintan Bhatt Assistant Professor
Keywords: Heat scintillation, atmospheric turbulence, motion vector calculation, registration, blind iterations, optical flow


Turbulence/Heat Scintillations is the change that arises in the refraction index of air with the temperature. The distortion created by the atmospheric turbulence is proportional to the distance between object and camera. In last few years, several approaches have been proposed to estimate and eliminate geometric distortion and blur. In this paper, a novel technique is proposed that improves the visual quality of video sequences affected by atmospheric turbulence. The proposed method is based on adaptive control grid interpolation (CGI). CGI approximates accurate motion vectors to generate a geometrically correct frame using certain reference frames. For high scintillation sequences, CGI doesn’t mitigate scintillations completely. The new methodology is proposed with updated trajectory estimation. The proposed method can effectively reduce the influence even for high atmospheric turbulence. Experimental results also prove that proposed approach is time efficient compared to traditional CGI.

Author Biographies

Akshay Patel, Post Graduate Student
Mr. Akshay Patel is currently doing his Post Graduation in Computer Engineering from Chandubhai S Patel Institute of Technology. His research area includes Computer Vision. Image Processing, Machine Learning.
Dippal Israni, Assistant Professor
Dippal Israni is currently working as an Assistant Professor at Chandubhai S. Patel Institute of Technology, CHARUSAT, Gujarat. He received his Bachelor of Engineering degree in Information Technology from Sardar Patel University, Gujarat (India), and Master of Technology degree in Computer Engineering from Dharmsinh Desai University, Gujarat (India). His research interests are Computer Vision, Image Processing, Machine Learning.
Nerella Arun Mani Kumar, Member (Research Staff)
Nerella Arun Mani Kumar is working as an Member(Research Staff) at Central Research Laboratory, Bharat Electronics Limited. His research interests are Hardware Design, Image Processing and Machine Learning.
Chintan Bhatt, Assistant Professor
Prof. Chintan Bhatt is currently working as an Assistant Professor in Computer Engineering department, Chandubhai S. Patel Institute of Technology, CHARUSAT. He is a member of IEEE, EAI, ACM, CSI, AIRCC (Academy & Industry Research Collaboration Center) and IAENG (International Association of Engineers). His areas of interest include Data Mining, Web Mining, Networking, Security Mobile Computing, Big Data, Image Processing and Software Engineering. He has more than 4 years of teaching experience and research experience, having good teaching and research interests.


N. Anantrasirichai, A. Achim, N. G. Kingsbury and D. R. Bull, Atmospheric Turbulence Mitigation Using Complex Wavelet-Based Fusion, IEEE Transactions on Image Processing, vol. 22, no. 6, pp. 2398-2408, June 2013.

D. H. Frakes, A New Method for Registration-Based Medical Image Interpolation, IEEE Transactions on Medical Imaging, vol. 27,no. 3, pp. 370-377, March 2008.

X. Zhu, P. Milanfar, Image Reconstruction from Videos Distorted by Atmospheric Turbulence, Proc. SPIE Electronic Imaging Conf.Visual Information Processing and Comm., Jan. 2010.

W. Zhang, Distortion-driven Turbulence Image Blur Effect Removal using Variational Model and Kernel Regression, 2014 International Conference on Identification, Information and Knowledge in the Internet of Things, Beijing, pp. 8-8, 2014.

X. Zhu, P. Milanfar, Stabilizing and deblurring atmospheric turbulence, IEEE International Conference on Computational Photography (ICCP), Pittsburgh-USA, pp. 1-8, 2011.

Y. Lou, S. H. Kang, S. Soatto, A. L. Bertozzi, Video stabilization of atmospheric turbulence distortion, Inverse Problems in Imaging, Special Issue in honor of Tony Chan,vol. 7, no. 3, pp. 839 - 861, August 2013.

K. Parmar, D. Israni, A. Shah, An efficient technique for subpixel accuracy using integrated feature based image registration, 2nd International Conference for Convergence in Technology (I2CT), Pune India, pp. 271-276, 2017.

Y. Chen, J. Wu, G. Yu, Adaptive Proximal Point Algorithms for Total Variation Image Restoration, Statistics, Optimization and Information Computing, Vol. 3, no. 1, pp 15C29, March 2015.

R. Chokshi, D. Israni and N. Chavda, An efficient deconvolution technique by identification and estimation of blur, 2016 IEEE International Conference on Recent Trends in Electronics, Information Communication Technology (RTEICT), Bangalore, pp. 17-23, 2016.

D. Fraser, G. Thorpe, and A. Lambert, Atmospheric turbulence visualization with wide-area motion-blur restoration, Journal of the Optical Society of America A, vol. 16, no. 7, pp. 1751-1758, 1999.

J. Kybic, M. Unser, Fast parametric elastic image registration, IEEE Transactions on Image Processing, vol. 12, no. 11, pp.1427-1442, Nov. 2003.

B. Fishbain, P. Leonid, Yaroslavsky, Ianir Ideses Spatial, Temporal, and Interchannel Image Data Fusion for Long-Distance Terrestrial Observation Systems, Advances in Optical Technologies, vol. 2008, Article ID 546808, pp. 1-18, 2008.

D. Kamenetsky, M. Zucchi, G. Nichols, D. Booth and A. Lambert, Interactive Atmospheric Turbulence Mitigation, International Conference on Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, QLD, pp. 1-8, 2016.

M. Moklyachuk, M. Sidei, Interpolation Problem for Stationary Sequences with Missing Observations, Statistics Optimization and Information Computing, Vol. 3, no. 3, pp 259C275, September 2015

M.Luz,M.Moklyachuk Minimax Interpolation Problem for Random Processes with Stationary Increments, Statistics,Optimization and Information Computing, vol. 3, no. 1, March 2015, pp 30C41.

G. J. Sullivan, R. L. Baker, Motion compensation for video compression using control grid interpolation, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Toronto, Canada, pp. 2713-2716, 1991.

D. H. Frakes, J. W. Monaco and M. J. T. Smith Suppression of atmospheric turbulence in video using an adaptive control grid interpolation approach, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), Salt Lake City, UT, 2001, pp. 1881-1884 vol.3.

J Gilles, N. B. Ferrante, Open Turbulent Image Set (OTIS), Journal of Pattern Recognition Letters, vol. 86, pp. 38-41, January 2017.

J. Ashburner J, K.J. Friston, Spatial transformation of images, Human Brain Function, edited by Frackowiak RSJ, Friston KJ, Frith CD, Dolan RJ, and Mazziotta JC, Academic Press, San Diego, London, pp. 43C58, 1997.

R. Abdoola, G. Noel, B. v. Wyk and E. Monacelli, Correction of Atmospheric Turbulence Degraded Sequences Using Grid Smoothing, 8th International Conference on Image Analysis and Recognition (ICIAR), Burnaby, BC, Canada, June 2011.

A.Pathak,A.Chaturvedi, Estimation of the reliability function for two-parameter exponentiated Rayleigh or Burr type X distribution, Statistics, Optimization and Information Computing, vol. 2, no. 4, pp. 305-322, December 2014

How to Cite
Patel, A., Israni, D., Mani Kumar, N. A., & Bhatt, C. (2019). An Adaptive Image Registration Technique to Remove Atmospheric Turbulence. Statistics, Optimization & Information Computing, 7(2), 439-446.
Research Articles