Comparing multiple turbulence restoration algorithms performance on noisy anisoplanatic imagery

In this paper, we compare the performance of multiple turbulence mitigation algorithms to restore imagery degraded by atmospheric turbulence and camera noise. In order to quantify and compare algorithm performance, imaging scenes were simulated by applying noise and varying levels of turbulence. For the simulation, a Monte-Carlo wave optics approach is used to simulate the spatially and temporally varying turbulence in an image sequence. A Poisson-Gaussian noise mixture model is then used to add noise to the observed turbulence image set. These degraded image sets are processed with three separate restoration algorithms: Lucky Look imaging, bispectral speckle imaging, and a block matching method with restoration filter. These algorithms were chosen because they incorporate different approaches and processing techniques. The results quantitatively show how well the algorithms are able to restore the simulated degraded imagery.

[1]  Mikhail A. Vorontsov,et al.  Anisoplanatic imaging through atmospheric turbulence: brightness function approach , 2007, SPIE Optical Engineering + Applications.

[2]  Carmen J. Carrano.  Speckle imaging over horizontal paths , 2002, SPIE Optics + Photonics.

[3]  A. Lambert,et al.  Atmospheric turbulence visualization with wide-area motion-blur restoration , 1999 .

[4]  Michael Rucci,et al.  Computationally efficient video restoration for Nyquist sampled imaging sensors combining an affine-motion-based temporal Kalman filter and adaptive Wiener filter. , 2014, Applied optics.

[5]  Russell C. Hardie,et al.  Block matching and Wiener filtering approach to optical turbulence mitigation and its application to simulated and real imagery with quantitative error analysis , 2017 .

[6]  Michael C. Roggemann,et al.  Comparison of bispectrum, multiframe blind deconvolution and hybrid bispectrum-multiframe blind deconvolution image reconstruction techniques for anisoplanatic, long horizontal-path imaging , 2014 .

[7]  Erik M. Johansson,et al.  Extended-image reconstruction through horizontal path turbulence using bispectral speckle interferometry , 1992 .

[8]  Alessandro Foi,et al.  Poisson-gaussian denoising using the exact unbiased inverse of the generalized anscombe transformation , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[9]  Russell C. Hardie,et al.  Simulation of anisoplanatic imaging through optical turbulence using numerical wave propagation with new validation analysis , 2017 .

[10]  B. Welsh,et al.  Imaging Through Turbulence , 1996 .

[11]  Stefano Soatto,et al.  Video stabilization of atmospheric turbulence distortion , 2013 .

[12]  Steven J. Simske,et al.  Atmospheric Turbulence-Degraded Image Restoration Using Principal Components Analysis , 2007, IEEE Geoscience and Remote Sensing Letters.

[13]  Mikhail A. Vorontsov,et al.  Automated video enhancement from a stream of atmospherically-distorted images: the lucky-region fusion approach , 2009, Optical Engineering + Applications.

[14]  Hai Liu,et al.  Atmospheric-Turbulence-Degraded Astronomical Image Restoration by Minimizing Second-Order Central Moment , 2012, IEEE Geoscience and Remote Sensing Letters.

[15]  D. Fried.  Limiting Resolution Looking Down Through the Atmosphere , 1966 .