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Dynamic quantitative phase imaging using deep spatial-temporal prior.

Siteng Li, Fei Wang, Zhenfeng Fu

|August 13, 2025

Summary

This summary is machine-generated.

This study introduces a faster deep learning method for quantitative phase imaging (QPI) using spatial-temporal prior (STeP) and a physics-enhanced neural network (PhysenNet). It significantly reduces computational costs and training time for dynamic object imaging.

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Area of Science:

Optics and Photonics
Biomedical Imaging
Artificial Intelligence

Background:

Non-interferometric deep learning-based quantitative phase imaging (QPI) offers label-free optical path length delay measurements.
Integrating deep learning with physical knowledge enhances QPI precision but faces lengthy optimization challenges, limiting multi-frame applications.

Purpose of the Study:

To develop an efficient method for QPI of dynamic objects using physics-enhanced neural networks.
To reduce computational costs and training time for multi-frame QPI tasks.

Main Methods:

Leveraged spatial-temporal prior (STeP) from video sequences.
Incorporated lightweight convolutional operations into a physics-enhanced neural network (PhysenNet).
Applied the method to QPI of dynamic objects without additional measurements.

Main Results:

Achieved accurate reconstructions of dynamic phase distributions.
Reduced computational costs and training time by over 90%.
Demonstrated effectiveness even under low signal-to-noise ratio conditions.

Conclusions:

The STeP-enhanced PhysenNet offers a significant advancement in efficient QPI for dynamic objects.
This method overcomes the limitations of lengthy optimization processes in previous deep learning QPI approaches.
Paves the way for practical, efficient multi-frame inverse imaging solutions.