Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networks

TL;DR

This paper introduces a convolutional recurrent neural network that rapidly reconstructs holographic images by recovering phase and amplitude from multiple holograms, enabling autofocusing and improving image quality in biomedical microscopy.

Contribution

It presents the first use of recurrent neural networks for holographic image reconstruction and phase recovery, enhancing speed and image quality over existing methods.

Findings

Successful imaging of human tissue and Pap smears

Improved image quality and depth-of-field

Faster inference compared to traditional methods

Abstract

Digital holography is one of the most widely used label-free microscopy techniques in biomedical imaging. Recovery of the missing phase information of a hologram is an important step in holographic image reconstruction. Here we demonstrate a convolutional recurrent neural network (RNN) based phase recovery approach that uses multiple holograms, captured at different sample-to-sensor distances to rapidly reconstruct the phase and amplitude information of a sample, while also performing autofocusing through the same network. We demonstrated the success of this deep learning-enabled holography method by imaging microscopic features of human tissue samples and Papanicolaou (Pap) smears. These results constitute the first demonstration of the use of recurrent neural networks for holographic imaging and phase recovery, and compared with existing methods, the presented approach improves the reconstructed image quality, while also increasing the depth-of-field and inference speed.