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Related Experiment Video

Updated: Jan 19, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Rapid and robust two-dimensional phase unwrapping via deep learning.

Teng Zhang, Shaowei Jiang, Zixin Zhao

|September 13, 2019

Summary

This summary is machine-generated.

A novel deep convolutional neural network (DCNN) method enhances two-dimensional phase unwrapping for optical metrology. This robust DCNN approach overcomes noise issues, improving accuracy in interference measurements.

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Last Updated: Jan 19, 2026

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Published on: November 11, 2022

Area of Science:

Optical Metrology
Image Processing
Computational Imaging

Background:

Two-dimensional phase unwrapping is crucial for optical metrology and measurements.
Conventional algorithms struggle with high noise levels in interference measurements, limiting their applicability.
Robust phase unwrapping is essential for accurate data acquisition in various scientific fields.

Purpose of the Study:

To introduce a rapid and robust two-dimensional phase unwrapping method using deep learning.
To address the limitations of conventional algorithms in noisy environments.
To improve the accuracy and reliability of phase unwrapping in optical metrology.

Main Methods:

A deep convolutional neural network (DCNN) architecture, specifically DeepLabV3+, was employed.
The DCNN was utilized for semantic segmentation of wrapped phase maps to suppress noise.
The segmentation results were combined with wrapped phase maps to generate unwrapped phases.

Main Results:

The proposed DCNN-based method demonstrated superior performance compared to conventional path-dependent and path-independent algorithms.
The approach showed significant robustness when tested with interference measurements from optical metrology setups.
The DCNN effectively suppressed noise and accurately represented features in phase maps.

Conclusions:

The developed DCNN method offers a significant advancement in two-dimensional phase unwrapping.
This technique provides a robust and accurate solution for noisy interference measurements.
Potential applications include optical metrology, microscopy imaging, and other related fields requiring precise phase analysis.