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A New Super Resolution Framework Based on Multi-Task Learning for Remote Sensing Images
Super-resolution (SR) algorithms based on deep learning have dominated in various tasks, including medical imaging, street view surveillance and face recognition. In the remote sensing field, most of the current SR methods utilize the low-resolution (LR) images that directly bicubic downsampled the...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959284/ https://www.ncbi.nlm.nih.gov/pubmed/33802432 http://dx.doi.org/10.3390/s21051743 |
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author | Yan, Li Chang, Kun |
author_facet | Yan, Li Chang, Kun |
author_sort | Yan, Li |
collection | PubMed |
description | Super-resolution (SR) algorithms based on deep learning have dominated in various tasks, including medical imaging, street view surveillance and face recognition. In the remote sensing field, most of the current SR methods utilize the low-resolution (LR) images that directly bicubic downsampled the high-resolution (HR) images as not only train set but also test set, thus achieving high PSNR/SSIM scores but showing performance drop in application because the degradation model in remote sensing images is subjected to Gaussian blur with unknown parameters. Inspired by multi-task learning strategy, we propose a multiple-blur-kernel super-resolution framework (MSF), in which a multiple-blur-kernel learning module (MLM) optimizes the parameters of the network transferable and sensitive for SR procedures with different blur kernels. Besides, to simultaneously exploit the prior of the large-scale remote sensing images and recurrent information in a single test image, a class-feature capture module (CCM) and an unsupervised learning module (ULM) are leveraged in our framework. Extensive experiments show that our framework outperforms the current state-of-the-art SR algorithms in remotely sensed imagery SR with unknown Gaussian blur kernel. |
format | Online Article Text |
id | pubmed-7959284 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79592842021-03-16 A New Super Resolution Framework Based on Multi-Task Learning for Remote Sensing Images Yan, Li Chang, Kun Sensors (Basel) Article Super-resolution (SR) algorithms based on deep learning have dominated in various tasks, including medical imaging, street view surveillance and face recognition. In the remote sensing field, most of the current SR methods utilize the low-resolution (LR) images that directly bicubic downsampled the high-resolution (HR) images as not only train set but also test set, thus achieving high PSNR/SSIM scores but showing performance drop in application because the degradation model in remote sensing images is subjected to Gaussian blur with unknown parameters. Inspired by multi-task learning strategy, we propose a multiple-blur-kernel super-resolution framework (MSF), in which a multiple-blur-kernel learning module (MLM) optimizes the parameters of the network transferable and sensitive for SR procedures with different blur kernels. Besides, to simultaneously exploit the prior of the large-scale remote sensing images and recurrent information in a single test image, a class-feature capture module (CCM) and an unsupervised learning module (ULM) are leveraged in our framework. Extensive experiments show that our framework outperforms the current state-of-the-art SR algorithms in remotely sensed imagery SR with unknown Gaussian blur kernel. MDPI 2021-03-03 /pmc/articles/PMC7959284/ /pubmed/33802432 http://dx.doi.org/10.3390/s21051743 Text en © 2021 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yan, Li Chang, Kun A New Super Resolution Framework Based on Multi-Task Learning for Remote Sensing Images |
title | A New Super Resolution Framework Based on Multi-Task Learning for Remote Sensing Images |
title_full | A New Super Resolution Framework Based on Multi-Task Learning for Remote Sensing Images |
title_fullStr | A New Super Resolution Framework Based on Multi-Task Learning for Remote Sensing Images |
title_full_unstemmed | A New Super Resolution Framework Based on Multi-Task Learning for Remote Sensing Images |
title_short | A New Super Resolution Framework Based on Multi-Task Learning for Remote Sensing Images |
title_sort | new super resolution framework based on multi-task learning for remote sensing images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959284/ https://www.ncbi.nlm.nih.gov/pubmed/33802432 http://dx.doi.org/10.3390/s21051743 |
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