Technique for simulating anisoplanatic image formation over long horizontal paths

Bos, Jeremy P.; Roggemann, Michael C.

doi:10.1117/1.oe.51.10.101704

Cited by 59 publications

(57 citation statements)

References 9 publications

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“…Previous synthesis methods of PSFs for turbulent atmospheric conditions use a C 2 n profile and wave propagation software to propagate a random wavefront perturbation of Zernike polynomial coefficients. 3,12,14 This is computationally intense. The synthesis procedure presented in Fig.…”

Section: Testing Of Synthesized Psfs For Statisticalmentioning

confidence: 99%

“…Ultimately, all of these phase impacts are summed and appropriately scaled and then used to produce a simulated image impacted by turbulence. 12 The summation of phase perturbations at the pupil plane of the optical system lends itself to representation by Zernike polynomials, which are defined in terms of radial and angular coordinates. Each of the Zernike terms describes an optical surface deviation related to classical aberration theory for optics.…”

Section: Modeling Of Atmospheric Turbulencementioning

confidence: 99%

“…10,11 The physical impacts of the phase screens on the wavefront are calculated using geometrical optics, and the resulting phase distortions accumulated, along with any unperturbed phase that is propagating from the object. 12 These accumulated phase perturbations are combined to create a wavefront specific to that location in space, then the wavefront is similarly propagated onward through space to the next phase screen. Ultimately, all of these phase impacts are summed and appropriately scaled and then used to produce a simulated image impacted by turbulence.…”

Section: Modeling Of Atmospheric Turbulencementioning

confidence: 99%

“…For example, for Zernike-based phase screens derived from wavefront propagation, the phase modeling must be propagated to the optical pupil plane and then propagated to the image plane to form a PSF. 12 Ideally, we want to have efficient computation of PSFs that are capable of characterizing the full range of turbulent conditions directly in the image plane. Meeting this requirement will provide computational speed improvements in the forward modeling steps of mitigation methods, such as MFBD.…”

Section: Modeling Of Atmospheric Turbulencementioning

confidence: 99%

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Synthesis of atmospheric turbulence point spread functions by sparse and redundant representations

et al. 2018

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Abstract. Atmospheric turbulence is a fundamental problem in imaging through long slant ranges, horizontalrange paths, or uplooking astronomical cases through the atmosphere. An essential characterization of atmospheric turbulence is the point spread function (PSF). Turbulence images can be simulated to study basic questions, such as image quality and image restoration, by synthesizing PSFs of desired properties. In this paper, we report on a method to synthesize PSFs of atmospheric turbulence. The method uses recent developments in sparse and redundant representations. From a training set of measured atmospheric PSFs, we construct a dictionary of "basis functions" that characterize the atmospheric turbulence PSFs. A PSF can be synthesized from this dictionary by a properly weighted combination of dictionary elements. We disclose an algorithm to synthesize PSFs from the dictionary. The algorithm can synthesize PSFs in three orders of magnitude less computing time than conventional wave optics propagation methods. The resulting PSFs are also shown to be statistically representative of the turbulence conditions that were used to construct the dictionary.

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Section: Testing Of Synthesized Psfs For Statisticalmentioning

confidence: 99%

Section: Modeling Of Atmospheric Turbulencementioning

confidence: 99%

Section: Modeling Of Atmospheric Turbulencementioning

confidence: 99%

Section: Modeling Of Atmospheric Turbulencementioning

confidence: 99%

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Synthesis of atmospheric turbulence point spread functions by sparse and redundant representations

et al. 2018

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“…13,17 The additive term α is a constant adjusted by the user that compensates for the fact that at high signal-to-noise ratios the PSD of the OTF may go to zero.…”

Section: Speckle Imagingmentioning

confidence: 99%

Comparison of bispectrum, multiframe blind deconvolution and hybrid bispectrum-multiframe blind deconvolution image reconstruction techniques for anisoplanatic, long horizontal-path imaging

2014

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Abstract. The potential benefits of real-time, or near-real-time, image processing hardware to correct for turbulence-induced image defects for long-range surveillance and weapons targeting are sufficient to motivate significant resource commitment to their development. Quantitative comparisons between potential candidates are necessary to decide on a preferred processing algorithm. We begin by comparing the mean-square-error (MSE) performance of speckle imaging (SI) methods and multiframe blind deconvolution (MFBD), applied to long-path horizontal imaging of a static scene under anisoplanatic seeing conditions. Both methods are used to reconstruct a scene from three sets of 1000 simulated images featuring low, moderate, and severe turbulence-induced aberrations. The comparison shows that SI techniques can reduce the MSE up to 47%, using 15 input frames under daytime conditions. The MFBD method provides up to 40% improvement in MSE under the same conditions. The performance comparison is repeated under three diminishing light conditions, 30, 15, 8 photons per pixel on average, where improvements of up to 39% can be achieved using SI methods with 25 input frames, and up to 38% for the MFBD method using 150 input frames. The MFBD estimator is applied to three sets of field data and representative results presented. Finally, the performance of a hybrid bispectrum-MFBD estimator that uses a rapid bispectrum estimate as the starting point for the MFBD image reconstruction algorithm is examined.

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Parallel hybrid bispectrum-multi-frame blind deconvolution image reconstruction technique

Hajmohammadi

Nooshabadi

Archer

et al. 2016

J Real-Time Image Proc

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This paper presents B-MFBD, a parallel hybrid of bispectrum speckle imaging (SI) and multi-frame blind deconvolution (MFBD) image reconstruction techniques for anisoplanatic, long horizontal path imaging. Our aimis to recover an enhanced version of a turbulence-corrupted image by massive parallelization of an SI and MFBD algorithms. The bispectrum SI technique is used in place of the multi-frame ensemble averaging to initialize the iterative parallel MFBD algorithm. B-MFBD technique, through massive parallelization, provides significantly large improvement in execution speed to both the bispectrum SI and MFBD parts of the hybrid algorithm. We report 85 % improvement in processing time with respect to the sequential implementation of the same algorithm for a 256 Â 256, gray-scale image, with comparable improvement in image quality.

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Technique for simulating anisoplanatic image formation over long horizontal paths

Cited by 59 publications

References 9 publications

Synthesis of atmospheric turbulence point spread functions by sparse and redundant representations

Synthesis of atmospheric turbulence point spread functions by sparse and redundant representations

Comparison of bispectrum, multiframe blind deconvolution and hybrid bispectrum-multiframe blind deconvolution image reconstruction techniques for anisoplanatic, long horizontal-path imaging

Parallel hybrid bispectrum-multi-frame blind deconvolution image reconstruction technique

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