EM based Framework for Single-shot Compressive Holography

TL;DR

This paper introduces a novel EM-based multiplicative gradient descent method for single-shot compressive holography, enabling faster, high-quality multi-depth and phase imaging from a single hologram, validated through simulations and experiments.

Contribution

It presents a new EM-based optimization framework that improves speed and quality of single-shot holographic reconstructions, including phase imaging capabilities.

Findings

Faster convergence compared to previous algorithms

Enhanced image quality in reconstructions

Successful phase imaging from a single hologram

Abstract

Lensless in-line holography is a simple, portable, and cost-effective method of imaging especially for the biomedical microscopy applications. We propose a multiplicative gradient descent optimization based method to obtain multi-depth imaging from a single hologram acquired in this imaging system. We further extend the method to achieve phase imaging from a single hologram. Negative-log-likelihood functional with the assumption of poisson noise has been used as the cost function to be minimized. The ill-posed nature of the problem is handled by the sparse regularization and the upper-bound constraint. The gradient descent optimization requires calculation of the partial derivative of the cost function with respect to a given estimate of the object. A method of obtaining this quantity for holography in both the cases of real object and complex object has been shown. The reconstruction method has been validated using extensive simulation and experimental studies. The comparison with the previously established iterative shrinkage/thresholding algorithm based compressive holography shows that the proposed method has the following advantages: significantly faster convergence rate, better reconstructed image quality and the ability to perform phase imaging.