In this paper, we achieve the shot-noise limit using straightforward image-post-processing techniques with experimental multi-shot digital holography data (i.e., off-axis data composed of multiple noise and speckle realizations). First, we quantify the effects of frame subtraction (of the mean reference-only frame and the mean signal-only frame from the digital-hologram frames), which boosts the signal-to-noise ratio (SNR) of the baseline dataset with a gain of 2.4 dB. Next, we quantify the effects of frame averaging, both with and without the frame subtraction. We show that even though the frame averaging boosts the SNR by itself, the frame subtraction and the stability of the digital-hologram fringes are necessary to achieve the shot-noise limit. Overall, we boost the SNR of the baseline dataset with a gain of 8.1 dB, which is the gain needed to achieve the shot-noise limit.

This paper uses an experimental setup consisting of phase plates and a digital-holography receiver to validate the performance of an algorithm, referred to as multi-plane iterative reconstruction (MIR), for imaging through deep turbulence. In general, deep-turbulence conditions arise from aberrations being distributed along the propagation path. The resulting phase errors then cause a multifaceted problem with multiple empirically determined limitations. To address these limitations, the MIR algorithm works by sensing and correcting for the distributed-volume phase errors using single-shot digital holography data (i.e., one speckle measurement from the coherent illumination of an optically rough extended object). As such, we first show that our distributed-volume phase errors, created using the phase plates, follow path-integrated Kolmogorov statistics for weak-to-deep turbulence strengths. We then present results from two MIR algorithm configurations: a) where we have a priori knowledge of the placement of the phase plates, so that we sense and correct in the exact locations of the phase errors, and b) where we do not have a priori knowledge of the placement of the phase plates, so that we sense and correct in two fixed planes for all phase-error combinations. Given weak-to-deep turbulence strengths, the results show that the two MIR algorithm configurations perform comparably for the four imaging scenarios tested. Such results are promising for tactical applications, where one might not have a priori knowledge of the deep-turbulence conditions.

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This paper compares the closed-loop, adaptive-optics-system performance obtained with a Shack-Hartmann wavefront sensor (WFS) to that of a fixed-pyramid WFS, assuming a point-source beacon. To quantify performance, the analysis uses wave-optics simulations and calculates the peak Strehl in weak to moderately deep turbulence conditions. Initial results show that the Shack-Hartmann WFS outperforms the fixed-pyramid WFS.