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High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

DeepGhost: real-time computational ghost imaging via deep learning.

Saad Rizvi¹, Jie Cao², Kaiyu Zhang¹

¹School of Optics and Photonics, Beijing Institute of Technology, Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing, 100081, China.

Scientific Reports

|July 11, 2020

Summary

This summary is machine-generated.

We developed DeepGhost, a fast computational ghost imaging (CGI) framework using deep learning. It achieves real-time imaging with low sampling rates, outperforming existing methods for complex scenes.

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Area of Science:

Optics and Photonics
Computer Vision
Machine Learning

Background:

Computational ghost imaging (CGI) offers potential for real-time applications but is limited by slow image reconstruction and poor performance with complex scenes.
Existing CGI methods struggle with efficiency, particularly at low sampling rates, hindering practical implementation.
Deep learning approaches have shown promise but often require extensive training data or shallow networks, limiting their effectiveness.

Purpose of the Study:

To introduce a novel, fast image reconstruction framework for computational ghost imaging (CGI) named DeepGhost.
To enable real-time CGI applications by significantly reducing image reconstruction time.
To enhance the reconstruction accuracy of complex and diverse scenes using minimal data.

Main Methods:

Utilized a deep convolutional autoencoder network architecture for image reconstruction.
Implemented a knowledge transfer strategy, leveraging the STL-10 dataset to train the physical-data driven network.
Achieved real-time imaging capabilities at significantly low sampling rates (10-20%).

Main Results:

Demonstrated high-accuracy reconstruction of complex, unseen targets.
Achieved real-time imaging performance, overcoming previous limitations of CGI.
Outperformed existing deep learning and state-of-the-art compressed sensing methods in ghost imaging under comparable conditions.
Successfully addressed shortcomings of prior methods, including inappropriate architectures and limited training data.

Conclusions:

DeepGhost provides a significant advancement in computational ghost imaging, enabling real-time applications.
The framework's deep architecture and efficient computation overcome the limitations of traditional and existing deep learning CGI methods.
This approach facilitates high-fidelity imaging even with very low data acquisition, opening new possibilities for CGI in various fields.