Speckle-free coherent imaging through deep turbulence.

We develop and validate a model-based iterative reconstruction framework for digitally correcting coherent images corrupted by deep turbulence. In general, this framework is applicable to coherent-imaging approaches that gain access to the complex-optical field; however, we demonstrate our approach with multi-shot digital holography data. To test our image correction framework, we generate calibrated deep-turbulence conditions from our laboratory testbed. Using the resulting data, we demonstrate groundbreaking performance in terms of speckle-free image correction in deep-turbulence conditions.

Imaging through deep turbulence using single-shot digital holography data.

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.

Imaging through distributed-volume aberrations using single-shot digital holography.

This paper explores the use of single-shot digital holography data and a novel algorithm, referred to as multiplane iterative reconstruction (MIR), for imaging through distributed-volume aberrations. Such aberrations result in a linear, shift-varying or "anisoplanatic" physical process, where multiple-look angles give rise to different point spread functions within the field of view of the imaging system. The MIR algorithm jointly computes the maximum a posteriori estimates of the anisoplanatic phase errors and the speckle-free object reflectance from the single-shot digital holography data. Using both simulations and experiments, we show that the MIR algorithm outperforms the leading multiplane image-sharpening algorithm over a wide range of anisoplanatic conditions.

Demonstration of single-shot digital holography using a Bayesian framework.

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.

Optically coherent image formation and denoising using a plug and play inversion framework.

The performance of optically coherent imaging systems can be limited by measurement and speckle noise. In this paper, we develop an image formation framework for computing the maximum a posteriori estimate of an object's reflectivity when imaged using coherent illumination and detection. The proposed approach allows for the use of Gaussian denoising algorithms (GDAs), without modification, to mitigate the exponentially distributed and signal-dependent noise that occurs in coherent imaging. Several GDAs are compared using both simulated and experimental data. The proposed framework is shown to be robust to noise and significantly reduce reconstruction error compared to the standard inversion technique.

Phase-error estimation and image reconstruction from digital-holography data using a Bayesian framework.

The estimation of phase errors from digital-holography data is critical for applications such as imaging or wavefront sensing. Conventional techniques require multiple i.i.d. data and perform poorly in the presence of high noise or large phase errors. In this paper, we propose a method to estimate isoplanatic phase errors from a single data realization. We develop a model-based iterative reconstruction algorithm that computes the maximum a posteriori estimate of the phase and the speckle-free object reflectance. Using simulated data, we show that the algorithm is robust against high noise and strong phase errors.