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Related Experiment Video

Updated: Jun 18, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Infrared Image Super-Resolution Network Utilizing the Enhanced Transformer and U-Net.

Feng Huang1, Yunxiang Li1, Xiaojing Ye1

  • 1School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China.

Sensors (Basel, Switzerland)
|July 27, 2024
PubMed
Summary

This study introduces SwinAIR-GAN, a novel deep learning method for infrared image super-resolution (SR). It enhances infrared image quality and detail reconstruction, addressing hardware cost limitations.

Keywords:
generative adversarial networkimage super-resolutioninfrared imagetransformer

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Infrared Imaging

Background:

  • Infrared images are crucial for remote sensing and fire safety but are limited by high hardware costs.
  • Deep learning-based super-resolution (SR) has advanced image reconstruction, yet infrared image SR remains underexplored.

Purpose of the Study:

  • To develop an effective deep learning model for infrared image super-resolution (SR).
  • To address the challenges of feature extraction, fusion, and artifact reduction in real infrared image SR.

Main Methods:

  • Designed the Residual Swin Transformer and Average Pooling Block (RSTAB) for feature extraction and fusion.
  • Proposed SwinAIR for superior infrared image SR reconstruction.
  • Developed SwinAIR-GAN by integrating SwinAIR with U-Net, incorporating spectral normalization, dropout, and artifact discrimination loss to simulate real infrared image degradation.

Main Results:

  • SwinAIR effectively extracts and fuses diverse frequency features for superior SR performance.
  • SwinAIR-GAN significantly improves real infrared image SR reconstruction.
  • The method successfully reconstructs realistic textures and details, validated by qualitative and quantitative evaluations.

Conclusions:

  • The proposed SwinAIR-GAN method offers an effective solution for infrared image super-resolution.
  • This approach overcomes limitations of high hardware costs by enhancing image quality through advanced deep learning techniques.
  • The method demonstrates robust performance in reconstructing realistic details in infrared imagery.