Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery

TL;DR

This paper introduces a deep learning method using CNNs to automatically focus and recover phase information in holography, greatly extending the depth-of-field and reducing reconstruction time.

Contribution

The authors develop a CNN-based approach that performs auto-focusing and phase-recovery simultaneously, significantly improving holographic image reconstruction efficiency and depth-of-field extension.

Findings

CNN achieves rapid phase-recovery and in-focus imaging from a single hologram.

Method reduces computational complexity from O(nm) to O(1).

Extended DOF enables better 3D sample visualization.

Abstract

Holography encodes the three dimensional (3D) information of a sample in the form of an intensity-only recording. However, to decode the original sample image from its hologram(s), auto-focusing and phase-recovery are needed, which are in general cumbersome and time-consuming to digitally perform. Here we demonstrate a convolutional neural network (CNN) based approach that simultaneously performs auto-focusing and phase-recovery to significantly extend the depth-of-field (DOF) in holographic image reconstruction. For this, a CNN is trained by using pairs of randomly de-focused back-propagated holograms and their corresponding in-focus phase-recovered images. After this training phase, the CNN takes a single back-propagated hologram of a 3D sample as input to rapidly achieve phase-recovery and reconstruct an in focus image of the sample over a significantly extended DOF. This deep learning based DOF extension method is non-iterative, and significantly improves the algorithm time-complexity of holographic image reconstruction from O(nm) to O(1), where n refers to the number of individual object points or particles within the sample volume, and m represents the focusing search space within which each object point or particle needs to be individually focused. These results highlight some of the unique opportunities created by data-enabled statistical image reconstruction methods powered by machine learning, and we believe that the presented approach can be broadly applicable to computationally extend the DOF of other imaging modalities.