Sharpness metric maximization is a method for reconstructing coherent
images that have been aberrated due to distributed-volume turbulence.
This method places one or more corrective phase screens in the
digital-propagation path that serve to increase overall sharpness of
the image. As such, this study uses sharpness metric maximization on
3D irradiances obtained via frequency-diverse digital holography. We
vary the number of corrective phase screens in the propagation path
and sharpen images of a realistic, extended object via multi-plane
sharpness metric maximization. The results indicate that image
reconstruction is possible when using fewer corrective screens than
aberrating screens, but that image quality increases with a greater
number of corrective screens.
This paper uses wave-optics and signal-to-noise models to explore the estimation accuracy of digital-holographic detection in the off-axis pupil plane recording geometry for deep-turbulence wavefront sensing. In turn, the analysis examines three important parameters: the number of pixels across the width of the focal-plane array, the window radius in the Fourier plane, and the signal-to-noise ratio. By varying these parameters, the wave-optics and signal-to-noise models quantify performance via a metric referred to as the field-estimated Strehl ratio, and the analysis leads to a method for optimal windowing of the turbulence-limited point spread function. Altogether, the results will allow future research efforts to assess the number of pixels, pixel size, pixel-well depth, and read-noise standard deviation needed from a focal-plane array when using digital-holographic detection in the off-axis pupil plane recording geometry for estimating the complex-optical field when in the presence of deep turbulence and detection noise.
In this paper, we present experimental results for image reconstruction, with isoplanatic phase-error correction, from single-shot digital holography data. We demonstrate the utility of using a model-based iterative reconstruction (MBIR) algorithm to jointly compute the maximum a posteriori estimates of the phase errors and the real-valued object reflectance function. Specifically, we show that the MBIR algorithm is robust to noise and phase errors over a range of conditions.
Using wave-optics simulations, this paper defines what subaperture
sampling effectively means for digital-holography applications
involving atmospheric turbulence. Throughout, we consider the on-axis
phase shifting recording geometry (PSRG) and off-axis PSRG, both with
the effects of sensor noise. The results ultimately show that
(1) insufficient subaperture sampling manifests as an efficiency loss
that limits the achievable signal-to-noise ratio and field-estimated
Strehl ratio; (2) digital-holography applications involving
atmospheric turbulence require at least three focal-plane array (FPA)
pixels per Fried coherence length to meet the Maréchal criterion; and
(3) off-axis PSRG is a valid and efficient implementation with minor
losses, as compared to on-axis PSRG. Such results will inform future
research efforts on how to efficiently use the available FPA
pixels.
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