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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Deep learning in optical metrology: a review.

Chao Zuo^1,2, Jiaming Qian^3,4, Shijie Feng^3,4

¹Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, 210094, Nanjing, Jiangsu Province, China. zuochao@njust.edu.cn.

Light, Science & Applications

|February 24, 2022

Summary

This summary is machine-generated.

Deep learning is revolutionizing optical metrology with data-driven solutions for tasks like fringe denoising and phase retrieval. This approach offers improved performance over traditional physics-based methods in various applications.

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Area of Science:

Optical metrology
Machine learning
Deep learning

Background:

Optical metrology is crucial for quality control, nondestructive testing, and biomedicine.
Deep learning, a subset of machine learning, excels at solving complex problems using data.
The integration of deep learning into optical metrology is rapidly advancing.

Purpose of the Study:

To provide an overview of deep learning in optical metrology.
To review the latest advancements and applications of deep learning technologies.
To discuss challenges and future research directions.

Main Methods:

Review of traditional image processing in optical metrology.
Introduction to fundamental deep learning concepts.
Comprehensive analysis of deep learning applications in optical metrology tasks.

Main Results:

Deep learning offers data-driven alternatives to physics-based methods.
Successful applications include fringe denoising, phase retrieval, and error compensation.
Deep learning demonstrates superior performance in various optical metrology tasks.

Conclusions:

Deep learning is a powerful tool transforming optical metrology.
Further research is needed to address current challenges.
Future directions include advancing deep learning methodologies for optical metrology.