The estimation of phase errors from digital-holography data is critical for applications such as imaging or wave-front 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 which 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.
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.
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.
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.
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