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Thermal Image Super-Resolution Based on Lightweight Dynamic Attention Network for Infrared Sensors

Infrared sensors capture infrared rays radiated by objects to form thermal images. They have a steady ability to penetrate smoke and fog, and are widely used in security monitoring, military, etc. However, civilian infrared detectors with lower resolution cannot compare with megapixel RGB camera sen...

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Detalles Bibliográficos
Autores principales: Zhang, Haikun, Hu, Yueli, Yan, Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10648050/
https://www.ncbi.nlm.nih.gov/pubmed/37960417
http://dx.doi.org/10.3390/s23218717
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author Zhang, Haikun
Hu, Yueli
Yan, Ming
author_facet Zhang, Haikun
Hu, Yueli
Yan, Ming
author_sort Zhang, Haikun
collection PubMed
description Infrared sensors capture infrared rays radiated by objects to form thermal images. They have a steady ability to penetrate smoke and fog, and are widely used in security monitoring, military, etc. However, civilian infrared detectors with lower resolution cannot compare with megapixel RGB camera sensors. In this paper, we propose a dynamic attention mechanism-based thermal image super-resolution network for infrared sensors. Specifically, the dynamic attention modules adaptively reweight the outputs of the attention and non-attention branches according to features at different depths of the network. The attention branch, which consists of channel- and pixel-wise attention blocks, is responsible for extracting the most informative features, while the non-attention branch is adopted as a supplement to extract the remaining ignored features. The dynamic weights block operates with 1D convolution instead of the full multi-layer perceptron on the global average pooled features, reducing parameters and enhancing information interaction between channels, and the same structure is adopted in the channel attention block. Qualitative and quantitative results on three testing datasets demonstrate that the proposed network can superior restore high-frequency details while improving the resolution of thermal images. And the lightweight structure of the proposed network with lower computing cost can be practically deployed on edge devices, effectively improving the imaging perception quality of infrared sensors.
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spelling pubmed-106480502023-10-25 Thermal Image Super-Resolution Based on Lightweight Dynamic Attention Network for Infrared Sensors Zhang, Haikun Hu, Yueli Yan, Ming Sensors (Basel) Article Infrared sensors capture infrared rays radiated by objects to form thermal images. They have a steady ability to penetrate smoke and fog, and are widely used in security monitoring, military, etc. However, civilian infrared detectors with lower resolution cannot compare with megapixel RGB camera sensors. In this paper, we propose a dynamic attention mechanism-based thermal image super-resolution network for infrared sensors. Specifically, the dynamic attention modules adaptively reweight the outputs of the attention and non-attention branches according to features at different depths of the network. The attention branch, which consists of channel- and pixel-wise attention blocks, is responsible for extracting the most informative features, while the non-attention branch is adopted as a supplement to extract the remaining ignored features. The dynamic weights block operates with 1D convolution instead of the full multi-layer perceptron on the global average pooled features, reducing parameters and enhancing information interaction between channels, and the same structure is adopted in the channel attention block. Qualitative and quantitative results on three testing datasets demonstrate that the proposed network can superior restore high-frequency details while improving the resolution of thermal images. And the lightweight structure of the proposed network with lower computing cost can be practically deployed on edge devices, effectively improving the imaging perception quality of infrared sensors. MDPI 2023-10-25 /pmc/articles/PMC10648050/ /pubmed/37960417 http://dx.doi.org/10.3390/s23218717 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Haikun
Hu, Yueli
Yan, Ming
Thermal Image Super-Resolution Based on Lightweight Dynamic Attention Network for Infrared Sensors
title Thermal Image Super-Resolution Based on Lightweight Dynamic Attention Network for Infrared Sensors
title_full Thermal Image Super-Resolution Based on Lightweight Dynamic Attention Network for Infrared Sensors
title_fullStr Thermal Image Super-Resolution Based on Lightweight Dynamic Attention Network for Infrared Sensors
title_full_unstemmed Thermal Image Super-Resolution Based on Lightweight Dynamic Attention Network for Infrared Sensors
title_short Thermal Image Super-Resolution Based on Lightweight Dynamic Attention Network for Infrared Sensors
title_sort thermal image super-resolution based on lightweight dynamic attention network for infrared sensors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10648050/
https://www.ncbi.nlm.nih.gov/pubmed/37960417
http://dx.doi.org/10.3390/s23218717
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AT huyueli thermalimagesuperresolutionbasedonlightweightdynamicattentionnetworkforinfraredsensors
AT yanming thermalimagesuperresolutionbasedonlightweightdynamicattentionnetworkforinfraredsensors