Super-Resolution of Thermal Images Using an Automatic Total Variation Based Method
The relatively poor spatial resolution of thermal images is a limitation for many thermal remote sensing applications. A possible solution to mitigate this problem is super-resolution, which should preserve the radiometric content of the original data and should be applied to both the cases where a...
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doaj-a452ab1fd53b436cb38a3b82ee0241b92020-11-25T02:18:54ZengMDPI AGRemote Sensing2072-42922020-05-01121642164210.3390/rs12101642Super-Resolution of Thermal Images Using an Automatic Total Variation Based MethodPasquale Cascarano0Francesco Corsini1Stefano Gandolfi2Elena Loli Piccolomini3Emanuele Mandanici4Luca Tavasci5Fabiana Zama6Department of Mathematics, University of Bologna, 40127 Bologna, Italy Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, 40136 Bologna, ItalyDepartment of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, 40136 Bologna, ItalyDepartment of Computer Science and Engineering, University of Bologna, 40127 Bologna, ItalyDepartment of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, 40136 Bologna, ItalyDepartment of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, 40136 Bologna, ItalyDepartment of Mathematics, University of Bologna, 40127 Bologna, Italy The relatively poor spatial resolution of thermal images is a limitation for many thermal remote sensing applications. A possible solution to mitigate this problem is super-resolution, which should preserve the radiometric content of the original data and should be applied to both the cases where a single image or multiple images of the target surface are available. In this perspective, we propose a new super-resolution algorithm, which can handle either single or multiple images. It is based on a total variation regularization approach and implements a fully automated choice of all the parameters, without any training dataset nor a priori information. Through simulations, the accuracy of the generated super-resolution images was assessed, in terms of both global statistical indicators and analysis of temperature errors at hot and cold spots. The algorithm was tested and applied to aerial and terrestrial thermal images. Results and comparisons with state-of-the-art methods confirmed an excellent compromise between the quality of the high-resolution images obtained and the required computational time.https://www.mdpi.com/2072-4292/12/10/1642super-resolutionthermal imagesregularized reconstructiontotal variation regularizationautomatic regularization |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Pasquale Cascarano Francesco Corsini Stefano Gandolfi Elena Loli Piccolomini Emanuele Mandanici Luca Tavasci Fabiana Zama |
spellingShingle |
Pasquale Cascarano Francesco Corsini Stefano Gandolfi Elena Loli Piccolomini Emanuele Mandanici Luca Tavasci Fabiana Zama Super-Resolution of Thermal Images Using an Automatic Total Variation Based Method Remote Sensing super-resolution thermal images regularized reconstruction total variation regularization automatic regularization |
author_facet |
Pasquale Cascarano Francesco Corsini Stefano Gandolfi Elena Loli Piccolomini Emanuele Mandanici Luca Tavasci Fabiana Zama |
author_sort |
Pasquale Cascarano |
title |
Super-Resolution of Thermal Images Using an Automatic Total Variation Based Method |
title_short |
Super-Resolution of Thermal Images Using an Automatic Total Variation Based Method |
title_full |
Super-Resolution of Thermal Images Using an Automatic Total Variation Based Method |
title_fullStr |
Super-Resolution of Thermal Images Using an Automatic Total Variation Based Method |
title_full_unstemmed |
Super-Resolution of Thermal Images Using an Automatic Total Variation Based Method |
title_sort |
super-resolution of thermal images using an automatic total variation based method |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2020-05-01 |
description |
The relatively poor spatial resolution of thermal images is a limitation for many thermal remote sensing applications. A possible solution to mitigate this problem is super-resolution, which should preserve the radiometric content of the original data and should be applied to both the cases where a single image or multiple images of the target surface are available. In this perspective, we propose a new super-resolution algorithm, which can handle either single or multiple images. It is based on a total variation regularization approach and implements a fully automated choice of all the parameters, without any training dataset nor a priori information. Through simulations, the accuracy of the generated super-resolution images was assessed, in terms of both global statistical indicators and analysis of temperature errors at hot and cold spots. The algorithm was tested and applied to aerial and terrestrial thermal images. Results and comparisons with state-of-the-art methods confirmed an excellent compromise between the quality of the high-resolution images obtained and the required computational time. |
topic |
super-resolution thermal images regularized reconstruction total variation regularization automatic regularization |
url |
https://www.mdpi.com/2072-4292/12/10/1642 |
work_keys_str_mv |
AT pasqualecascarano superresolutionofthermalimagesusinganautomatictotalvariationbasedmethod AT francescocorsini superresolutionofthermalimagesusinganautomatictotalvariationbasedmethod AT stefanogandolfi superresolutionofthermalimagesusinganautomatictotalvariationbasedmethod AT elenalolipiccolomini superresolutionofthermalimagesusinganautomatictotalvariationbasedmethod AT emanuelemandanici superresolutionofthermalimagesusinganautomatictotalvariationbasedmethod AT lucatavasci superresolutionofthermalimagesusinganautomatictotalvariationbasedmethod AT fabianazama superresolutionofthermalimagesusinganautomatictotalvariationbasedmethod |
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1724880038259589120 |