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A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution.

Rafael E Rivadeneira1, Angel D Sappa1,2, Boris X Vintimilla1

  • 1Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería en Electricidad y Computación, CIDIS, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil 090112, Ecuador.

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Summary
This summary is machine-generated.

This study introduces a new method to improve low-resolution thermal images using a CycleGAN architecture. The technique enhances thermal image resolution, outperforming existing methods in a recent challenge.

Keywords:
attention modulesemiregistered thermal imagesthermal image super-resolutionthermal imagesunsupervised super-resolution

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Low-resolution thermal imaging presents challenges in detail and quality.
  • Existing super-resolution methods struggle with thermal image domain limitations.

Purpose of the Study:

  • To develop a transfer domain strategy for enhancing thermal image resolution.
  • To address the limitations of low-resolution thermal sensors.

Main Methods:

  • Utilized a CycleGAN architecture with a ResNet encoder and an attention module.
  • Introduced a novel loss function for improved image generation.
  • Trained the network on a multi-resolution thermal image dataset from three sensors.

Main Results:

  • Achieved superior performance in thermal image super-resolution.
  • Outperformed state-of-the-art methods in the 2nd CVPR-PBVS-2021 challenge.
  • Generated higher-resolution thermal images of reasonable quality.

Conclusions:

  • The proposed transfer domain strategy effectively enhances thermal image resolution.
  • The method offers a viable solution for improving thermal imaging applications.
  • The developed technique surpasses current benchmarks in thermal image super-resolution.