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Related Experiment Video

Updated: Feb 9, 2026

Generation of Shear Adhesion Map Using SynVivo Synthetic Microvascular Networks

Generation of Shear Adhesion Map Using SynVivo Synthetic Microvascular Networks

Published on: May 25, 2014

Digital radiography image denoising using a generative adversarial network.

Yuewen Sun^1,2, Ximing Liu^1,2, Peng Cong^1,2

¹Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China.

Journal of X-Ray Science and Technology

|June 12, 2018

Summary

This summary is machine-generated.

A new generative adversarial network (GAN) method effectively denoises digital radiography (DR) x-ray images. This approach removes statistical noise while preserving image details, outperforming traditional methods for security applications.

Keywords:

Digital radiography generative adversarial network image denoising

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Functional Calcium Imaging in Developing Cortical Networks

Functional Calcium Imaging in Developing Cortical Networks

Published on: October 22, 2011

Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Statistical noise degrades digital radiography (DR) x-ray image quality.
Traditional methods to reduce noise involve increasing exposure time or radiation intensity.
Rapid, low-dose detection is crucial for applications like security checks and medical examinations.

Purpose of the Study:

To propose and evaluate a generative adversarial network (GAN) based method for x-ray image denoising.
To address the limitations of current methods in rapid, low-dose imaging scenarios.
To enhance the quality of x-ray images for security screening applications.

Main Methods:

Utilized a generative adversarial network (GAN) for x-ray image denoising.
Acquired images from a digital radiography (DR) imaging system for experimentation.
Compared the proposed GAN-based method against traditional convolutional neural network (CNN) based approaches.

Main Results:

The GAN-based method effectively removed statistical noise from x-ray images.
The denoising process preserved sharp edges and clear structural details.
The proposed method generated more plausible and detailed images compared to CNN-based methods.

Conclusions:

The proposed GAN-based denoising method is a promising technique for improving x-ray image quality.
This method offers a viable solution for rapid, low-dose x-ray imaging applications.
The GAN approach surpasses traditional CNN methods in generating high-fidelity denoised x-ray images.