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Research on Blind Super-Resolution Technology for Infrared Images of Power Equipment Based on Compressed Sensing Theory

Infrared images of power equipment play an important role in power equipment status monitoring and fault identification. Aiming to resolve the problems of low resolution and insufficient clarity in the application of infrared images, we propose a blind super-resolution algorithm based on the theory...

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Detalles Bibliográficos
Autores principales: Wang, Yan, Wang, Lingjie, Liu, Bingcong, Zhao, Hongshan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8232595/
https://www.ncbi.nlm.nih.gov/pubmed/34203747
http://dx.doi.org/10.3390/s21124109
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author Wang, Yan
Wang, Lingjie
Liu, Bingcong
Zhao, Hongshan
author_facet Wang, Yan
Wang, Lingjie
Liu, Bingcong
Zhao, Hongshan
author_sort Wang, Yan
collection PubMed
description Infrared images of power equipment play an important role in power equipment status monitoring and fault identification. Aiming to resolve the problems of low resolution and insufficient clarity in the application of infrared images, we propose a blind super-resolution algorithm based on the theory of compressed sensing. It includes an improved blur kernel estimation method combined with compressed sensing theory and an improved infrared image super-resolution reconstruction algorithm based on block compressed sensing theory. In the blur kernel estimation method, we propose a blur kernel estimation algorithm under the compressed sensing framework to realize the estimation of the blur kernel from low-resolution images. In the estimation process, we define a new [Formula: see text] norm to constrain the gradient image in the iterative process by analyzing the significant edge intensity changes before and after the image is blurred. With the [Formula: see text] norm, the salient edges can be selected and enhanced, the intermediate latent image generated by the iteration can move closer to the clear image, and the accuracy of the blur kernel estimation can be improved. For the super-resolution reconstruction algorithm, we introduce a blur matrix and a regular total variation term into the traditional compressed sensing model and design a two-step total variation sparse iteration (TwTVSI) algorithm. Therefore, while ensuring the computational efficiency, the boundary effect caused by the block processing inside the image is removed. In addition, the design of the TwTVSI algorithm can effectively process the super-resolution model of compressed sensing with a sparse dictionary, thereby breaking through the reconstruction performance limitation of the traditional regularized super-resolution method of compressed sensing due to the lack of sparseness in the signal transform domain. The final experimental results also verify the effectiveness of our blind super-resolution algorithm.
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spelling pubmed-82325952021-06-26 Research on Blind Super-Resolution Technology for Infrared Images of Power Equipment Based on Compressed Sensing Theory Wang, Yan Wang, Lingjie Liu, Bingcong Zhao, Hongshan Sensors (Basel) Article Infrared images of power equipment play an important role in power equipment status monitoring and fault identification. Aiming to resolve the problems of low resolution and insufficient clarity in the application of infrared images, we propose a blind super-resolution algorithm based on the theory of compressed sensing. It includes an improved blur kernel estimation method combined with compressed sensing theory and an improved infrared image super-resolution reconstruction algorithm based on block compressed sensing theory. In the blur kernel estimation method, we propose a blur kernel estimation algorithm under the compressed sensing framework to realize the estimation of the blur kernel from low-resolution images. In the estimation process, we define a new [Formula: see text] norm to constrain the gradient image in the iterative process by analyzing the significant edge intensity changes before and after the image is blurred. With the [Formula: see text] norm, the salient edges can be selected and enhanced, the intermediate latent image generated by the iteration can move closer to the clear image, and the accuracy of the blur kernel estimation can be improved. For the super-resolution reconstruction algorithm, we introduce a blur matrix and a regular total variation term into the traditional compressed sensing model and design a two-step total variation sparse iteration (TwTVSI) algorithm. Therefore, while ensuring the computational efficiency, the boundary effect caused by the block processing inside the image is removed. In addition, the design of the TwTVSI algorithm can effectively process the super-resolution model of compressed sensing with a sparse dictionary, thereby breaking through the reconstruction performance limitation of the traditional regularized super-resolution method of compressed sensing due to the lack of sparseness in the signal transform domain. The final experimental results also verify the effectiveness of our blind super-resolution algorithm. MDPI 2021-06-15 /pmc/articles/PMC8232595/ /pubmed/34203747 http://dx.doi.org/10.3390/s21124109 Text en © 2021 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
Wang, Yan
Wang, Lingjie
Liu, Bingcong
Zhao, Hongshan
Research on Blind Super-Resolution Technology for Infrared Images of Power Equipment Based on Compressed Sensing Theory
title Research on Blind Super-Resolution Technology for Infrared Images of Power Equipment Based on Compressed Sensing Theory
title_full Research on Blind Super-Resolution Technology for Infrared Images of Power Equipment Based on Compressed Sensing Theory
title_fullStr Research on Blind Super-Resolution Technology for Infrared Images of Power Equipment Based on Compressed Sensing Theory
title_full_unstemmed Research on Blind Super-Resolution Technology for Infrared Images of Power Equipment Based on Compressed Sensing Theory
title_short Research on Blind Super-Resolution Technology for Infrared Images of Power Equipment Based on Compressed Sensing Theory
title_sort research on blind super-resolution technology for infrared images of power equipment based on compressed sensing theory
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8232595/
https://www.ncbi.nlm.nih.gov/pubmed/34203747
http://dx.doi.org/10.3390/s21124109
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