Practical aspects of image recovery by means of the bispectrum

Negrete-Regagnon, Pedro

doi:10.1364/josaa.13.001557

“…Exact and approximate expressions for the error-probability performances of these structures were derived, and simulations were carried out to verify the analytic performance calculations. Realistic sample functions of turbulence-degraded focal-plane signal distributions were generated using the Kolmogorov phase-screen algorithms described in [7], corresponding to moderate daytime turbulence (a coherence length of 4 cm), and used to evaluate the performance of optimum and suboptimum array detection algorithms designed for PPM signals. Performance improvements of up to 5 dB were demonstrated over a single large detector designed to collect most of the turbulent signal, when operating in the presence of moderate to strong background radiation.…”

Section: Discussionmentioning

confidence: 99%

“…Two different signal models were used: a simple test model wherein only 5 of the 16 × 16 = 256 total detector elements were assumed to contain signal energy while the rest were assumed to contain no signal, and a more realistic 16 × 16 detector array model wherein the signal distribution over the array was simulated using a Kolmogorov turbulence model as described in [7] and all 256 detector elements may contain some signal. (red squares represent computed and light blue lines simulated data) yields somewhat better performance than the suboptimum 0-1 subarray computed according to Eq.…”

Section: H Comparison Of Exact and Approximate Performance Calculationsmentioning

confidence: 99%

“…In order to generate a spatial distribution of the signal incident upon the detector plane, a sample field was generated using a Kolmogorov phase-screen program [7], resulting in a matrix of complex signal amplitudes. For the simulation, an atmospheric correlation length of r 0 = 4 cm was assumed, which implies that the results should apply to any receiving aperture that is much greater than this correlation length [1].…”

Section: B Performance Of Adaptive Synthesized Single-detector Receivermentioning

confidence: 99%

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Adaptive detector arrays for optical communications receivers

Vilnrotter

¹

,

Srinivasan

²

2002

IEEE Trans. Commun.

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“…However, instead of directly solving for the minimum of the error, they performed a gradient-guided search in the high-dimensional space spanned by the object spectrum phase elements (Negrete-Regagnon 1996). Done this way, there are myriad local minima en route to the true minimum where all the gradients of the error function are zero.…”

Section: Introductionmentioning

confidence: 99%

New phasor reconstruction for speckle imaging

Dente

¹

,

Tilton

²

2011

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We will develop and then compare object spectrum phasor reconstruction results for several speckle imaging approaches. Each phasor reconstruction algorithm results from minimizing a very naturally defined weighted-least-squares error function. Once we pick a phasor-based error function, the remaining steps in our algorithms are developed by setting the error function variation, with respect to each phasor element, to zero. The resulting coupled nonlinear equations for the minimum error phasor array are then solved iteratively. In the applications, we will specifically compare and contrast three implementations: 1) Knox-Thompson; 2) bispectrum, using only two bispectrum planes; 3) bispectrum, using four bispectrum planes. In each application of the three approaches, we first calculate the modulus of the object spectrum using a Wiener-Helstrom filter to remove the speckle transfer function. The methods then differ only in their object spectrum phasor reconstructions. In the simulations, we will implement all three methods on a simple object at low photon-per-frame light levels. Next, we will apply the methods to a complex extended object. Although we develop and minimize error functions for three specific speckle methods, the approach readily generalizes to other cases.

show abstract

“…Done this way, there are myriad local minima en route to the true minimum where all the gradients of the error function are zero. Searching for the minimum error in this way can be a poor choice of algorithm that can easily stagnate (Negrete-Regagnon 1996).…”

Section: Introductionmentioning

confidence: 99%

New phasor reconstruction for speckle imaging

Dente¹,

Tilton

²

2011

A&A

1

0

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We will develop and then compare object spectrum phasor reconstruction results for several speckle imaging approaches. Each phasor reconstruction algorithm results from minimizing a very naturally defined weighted-least-squares error function. Once we pick a phasor-based error function, the remaining steps in our algorithms are developed by setting the error function variation, with respect to each phasor element, to zero. The resulting coupled nonlinear equations for the minimum error phasor array are then solved iteratively. In the applications, we will specifically compare and contrast three implementations: 1) Knox-Thompson; 2) bispectrum, using only two bispectrum planes; 3) bispectrum, using four bispectrum planes. In each application of the three approaches, we first calculate the modulus of the object spectrum using a Wiener-Helstrom filter to remove the speckle transfer function. The methods then differ only in their object spectrum phasor reconstructions. In the simulations, we will implement all three methods on a simple object at low photon-per-frame light levels. Next, we will apply the methods to a complex extended object. Although we develop and minimize error functions for three specific speckle methods, the approach readily generalizes to other cases.

show abstract

Practical aspects of image recovery by means of the bispectrum

Cited by 33 publications

References 28 publications

Adaptive detector arrays for optical communications receivers

Adaptive detector arrays for optical communications receivers

New phasor reconstruction for speckle imaging

New phasor reconstruction for speckle imaging

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