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A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution
by
Rivadeneira, Rafael E.
, Hammoud, Riad
, Sappa, Angel D.
, Vintimilla, Boris X.
in
Algorithms
/ attention module
/ Cameras
/ Datasets
/ Deep learning
/ Image Processing, Computer-Assisted - methods
/ Magnetic Resonance Imaging - methods
/ Medical imaging
/ Neural networks
/ semiregistered thermal images
/ thermal image super-resolution
/ thermal images
/ unsupervised super-resolution
/ Workshops
2022
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A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution
by
Rivadeneira, Rafael E.
, Hammoud, Riad
, Sappa, Angel D.
, Vintimilla, Boris X.
in
Algorithms
/ attention module
/ Cameras
/ Datasets
/ Deep learning
/ Image Processing, Computer-Assisted - methods
/ Magnetic Resonance Imaging - methods
/ Medical imaging
/ Neural networks
/ semiregistered thermal images
/ thermal image super-resolution
/ thermal images
/ unsupervised super-resolution
/ Workshops
2022
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Do you wish to request the book?
A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution
by
Rivadeneira, Rafael E.
, Hammoud, Riad
, Sappa, Angel D.
, Vintimilla, Boris X.
in
Algorithms
/ attention module
/ Cameras
/ Datasets
/ Deep learning
/ Image Processing, Computer-Assisted - methods
/ Magnetic Resonance Imaging - methods
/ Medical imaging
/ Neural networks
/ semiregistered thermal images
/ thermal image super-resolution
/ thermal images
/ unsupervised super-resolution
/ Workshops
2022
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A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution
Journal Article
A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution
2022
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Overview
This paper presents a transfer domain strategy to tackle the limitations of low-resolution thermal sensors and generate higher-resolution images of reasonable quality. The proposed technique employs a CycleGAN architecture and uses a ResNet as an encoder in the generator along with an attention module and a novel loss function. The network is trained on a multi-resolution thermal image dataset acquired with three different thermal sensors. Results report better performance benchmarking results on the 2nd CVPR-PBVS-2021 thermal image super-resolution challenge than state-of-the-art methods. The code of this work is available online.
Publisher
MDPI AG,MDPI
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