Lightweight Super-Resolution Reconstruction Vision Transformers of Remote Sensing Image Based on Structural Re-Parameterization

In recent times, remote sensing image super-resolution reconstruction technology based on deep learning has experienced rapid development. However, most algorithms in this domain concentrate solely on enhancing the super-resolution network’s performance while neglecting the equally crucial aspect of...
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Gespeichert in:

Autor*in:	Jiaming Bian [verfasserIn] Ye Liu [verfasserIn] Jun Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	remote sensing image super resolution reconstruction vision transformers structural re-parameterization

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 14(2024), 2, p 917
Übergeordnetes Werk:	volume:14 ; year:2024 ; number:2, p 917

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app14020917

Katalog-ID:	DOAJ09619832X

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language	English
source	In Applied Sciences 14(2024), 2, p 917 volume:14 year:2024 number:2, p 917
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format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	remote sensing image super resolution reconstruction vision transformers structural re-parameterization Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry
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container_title	Applied Sciences
authorswithroles_txt_mv	Jiaming Bian @@aut@@ Ye Liu @@aut@@ Jun Chen @@aut@@
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callnumber-first	T - Technology
author	Jiaming Bian
spellingShingle	Jiaming Bian misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc remote sensing image misc super resolution reconstruction misc vision transformers misc structural re-parameterization misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry Lightweight Super-Resolution Reconstruction Vision Transformers of Remote Sensing Image Based on Structural Re-Parameterization
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topic_title	TA1-2040 QH301-705.5 QC1-999 QD1-999 Lightweight Super-Resolution Reconstruction Vision Transformers of Remote Sensing Image Based on Structural Re-Parameterization remote sensing image super resolution reconstruction vision transformers structural re-parameterization
topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc remote sensing image misc super resolution reconstruction misc vision transformers misc structural re-parameterization misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title	Lightweight Super-Resolution Reconstruction Vision Transformers of Remote Sensing Image Based on Structural Re-Parameterization
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title_sort	lightweight super-resolution reconstruction vision transformers of remote sensing image based on structural re-parameterization
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title_auth	Lightweight Super-Resolution Reconstruction Vision Transformers of Remote Sensing Image Based on Structural Re-Parameterization
abstract	In recent times, remote sensing image super-resolution reconstruction technology based on deep learning has experienced rapid development. However, most algorithms in this domain concentrate solely on enhancing the super-resolution network’s performance while neglecting the equally crucial aspect of inference speed. In this study, we propose a method for lightweight super-resolution reconstruction of remote sensing images, termed SRRepViT. This approach reduces model parameters and floating-point operations during inference through parameter equivalent transformation. Using the RSSOD remote sensing dataset as our benchmark dataset, we compared the reconstruction performance, inference time, and model size of SRRepViT with other classical methods. Compared to the lightweight model ECBSR, SRRepViT exhibits slightly improved reconstruction performance while reducing inference time by 16% and model parameters by 34%, respectively. Moreover, compared to other classical super-resolution reconstruction methods, the SRRepViT model achieves similar reconstruction performance while reducing model parameters by 98% and increasing inference speed by 90% for a single remote sensing image.
abstractGer	In recent times, remote sensing image super-resolution reconstruction technology based on deep learning has experienced rapid development. However, most algorithms in this domain concentrate solely on enhancing the super-resolution network’s performance while neglecting the equally crucial aspect of inference speed. In this study, we propose a method for lightweight super-resolution reconstruction of remote sensing images, termed SRRepViT. This approach reduces model parameters and floating-point operations during inference through parameter equivalent transformation. Using the RSSOD remote sensing dataset as our benchmark dataset, we compared the reconstruction performance, inference time, and model size of SRRepViT with other classical methods. Compared to the lightweight model ECBSR, SRRepViT exhibits slightly improved reconstruction performance while reducing inference time by 16% and model parameters by 34%, respectively. Moreover, compared to other classical super-resolution reconstruction methods, the SRRepViT model achieves similar reconstruction performance while reducing model parameters by 98% and increasing inference speed by 90% for a single remote sensing image.
abstract_unstemmed	In recent times, remote sensing image super-resolution reconstruction technology based on deep learning has experienced rapid development. However, most algorithms in this domain concentrate solely on enhancing the super-resolution network’s performance while neglecting the equally crucial aspect of inference speed. In this study, we propose a method for lightweight super-resolution reconstruction of remote sensing images, termed SRRepViT. This approach reduces model parameters and floating-point operations during inference through parameter equivalent transformation. Using the RSSOD remote sensing dataset as our benchmark dataset, we compared the reconstruction performance, inference time, and model size of SRRepViT with other classical methods. Compared to the lightweight model ECBSR, SRRepViT exhibits slightly improved reconstruction performance while reducing inference time by 16% and model parameters by 34%, respectively. Moreover, compared to other classical super-resolution reconstruction methods, the SRRepViT model achieves similar reconstruction performance while reducing model parameters by 98% and increasing inference speed by 90% for a single remote sensing image.
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title_short	Lightweight Super-Resolution Reconstruction Vision Transformers of Remote Sensing Image Based on Structural Re-Parameterization
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