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Updated: Jul 3, 2025

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

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Published on: November 30, 2022

J-Net: Improved U-Net for Terahertz Image Super-Resolution.

Woon-Ha Yeo^1,2, Seung-Hwan Jung^1,2, Seung Jae Oh³

¹Department of Artificial Intelligence Convergence, Sahmyook University, 815 Hwarang-ro, Nowon-gu, Seoul 01795, Republic of Korea.

Sensors (Basel, Switzerland)

|February 10, 2024

Summary

This summary is machine-generated.

Researchers developed J-Net, a novel deep learning network, to enhance terahertz (THz) image resolution. This method significantly improves image quality for various applications, outperforming existing super-resolution techniques.

Keywords:

convolutional neural network (CNN)deep learning image super-resolution terahertz images

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Published on: February 12, 2014

Area of Science:

Electromagnetic Wave Applications
Image Processing and Computer Vision

Background:

Terahertz (THz) waves (0.1–10 THz) are used in security, biomedical, and materials examination.
Low resolution of THz images, caused by long wavelengths, limits their practical application.
Improving THz image resolution is a critical research challenge.

Purpose of the Study:

To introduce J-Net, a novel network architecture for enhancing terahertz image super-resolution.
To efficiently extract low-resolution features and map them to high-resolution images.

Main Methods:

Proposed J-Net, an enhanced U-Net architecture with simple baseline blocks.
Trained the network on the DIV2K+Flickr2K dataset.
Evaluated performance using Peak Signal-to-Noise Ratio (PSNR) and visual inspection.

Main Results:

J-Net achieved a PSNR of 32.52 dB, exceeding other methods by over 1 dB.
Demonstrated superior performance on real-world THz images compared to existing techniques.
Showcased significant visual improvements in super-resolved THz images.

Conclusions:

J-Net effectively addresses the challenge of low-resolution THz imaging.
The proposed architecture offers superior quantitative and qualitative results for THz image super-resolution.
J-Net represents a significant advancement in THz imaging technology.