Infer Thermal Information from Visual Information: A Cross Imaging Modality Edge Learning (CIMEL) Framework

The measurement accuracy and reliability of thermography is largely limited by a relatively low spatial-resolution of infrared (IR) cameras in comparison to digital cameras. Using a high-end IR camera to achieve high spatial-resolution can be costly or sometimes infeasible due to the high sample rat...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Shuozhi Wang, Jianqiang Mei, Lichao Yang, Yifan Zhao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	image enhancement edge detection deep learning thermography Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/7b02ecd9a61a4b4a9a3b12daa3483b8f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7b02ecd9a61a4b4a9a3b12daa3483b8f
record_format	dspace
spelling	oai:doaj.org-article:7b02ecd9a61a4b4a9a3b12daa3483b8f2021-11-25T18:56:43ZInfer Thermal Information from Visual Information: A Cross Imaging Modality Edge Learning (CIMEL) Framework10.3390/s212274711424-8220https://doaj.org/article/7b02ecd9a61a4b4a9a3b12daa3483b8f2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7471https://doaj.org/toc/1424-8220The measurement accuracy and reliability of thermography is largely limited by a relatively low spatial-resolution of infrared (IR) cameras in comparison to digital cameras. Using a high-end IR camera to achieve high spatial-resolution can be costly or sometimes infeasible due to the high sample rate required. Therefore, there is a strong demand to improve the quality of IR images, particularly on edges, without upgrading the hardware in the context of surveillance and industrial inspection systems. This paper proposes a novel Conditional Generative Adversarial Networks (CGAN)-based framework to enhance IR edges by learning high-frequency features from corresponding visual images. A dual-discriminator, focusing on edge and content/background, is introduced to guide the cross imaging modality learning procedure of the U-Net generator in high and low frequencies respectively. Results demonstrate that the proposed framework can effectively enhance barely visible edges in IR images without introducing artefacts, meanwhile the content information is well preserved. Different from most similar studies, this method only requires IR images for testing, which will increase the applicability of some scenarios where only one imaging modality is available, such as active thermography.Shuozhi WangJianqiang MeiLichao YangYifan ZhaoMDPI AGarticleimage enhancementedge detectiondeep learningthermographyChemical technologyTP1-1185ENSensors, Vol 21, Iss 7471, p 7471 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	image enhancement edge detection deep learning thermography Chemical technology TP1-1185
spellingShingle	image enhancement edge detection deep learning thermography Chemical technology TP1-1185 Shuozhi Wang Jianqiang Mei Lichao Yang Yifan Zhao Infer Thermal Information from Visual Information: A Cross Imaging Modality Edge Learning (CIMEL) Framework
description	The measurement accuracy and reliability of thermography is largely limited by a relatively low spatial-resolution of infrared (IR) cameras in comparison to digital cameras. Using a high-end IR camera to achieve high spatial-resolution can be costly or sometimes infeasible due to the high sample rate required. Therefore, there is a strong demand to improve the quality of IR images, particularly on edges, without upgrading the hardware in the context of surveillance and industrial inspection systems. This paper proposes a novel Conditional Generative Adversarial Networks (CGAN)-based framework to enhance IR edges by learning high-frequency features from corresponding visual images. A dual-discriminator, focusing on edge and content/background, is introduced to guide the cross imaging modality learning procedure of the U-Net generator in high and low frequencies respectively. Results demonstrate that the proposed framework can effectively enhance barely visible edges in IR images without introducing artefacts, meanwhile the content information is well preserved. Different from most similar studies, this method only requires IR images for testing, which will increase the applicability of some scenarios where only one imaging modality is available, such as active thermography.
format	article
author	Shuozhi Wang Jianqiang Mei Lichao Yang Yifan Zhao
author_facet	Shuozhi Wang Jianqiang Mei Lichao Yang Yifan Zhao
author_sort	Shuozhi Wang
title	Infer Thermal Information from Visual Information: A Cross Imaging Modality Edge Learning (CIMEL) Framework
title_short	Infer Thermal Information from Visual Information: A Cross Imaging Modality Edge Learning (CIMEL) Framework
title_full	Infer Thermal Information from Visual Information: A Cross Imaging Modality Edge Learning (CIMEL) Framework
title_fullStr	Infer Thermal Information from Visual Information: A Cross Imaging Modality Edge Learning (CIMEL) Framework
title_full_unstemmed	Infer Thermal Information from Visual Information: A Cross Imaging Modality Edge Learning (CIMEL) Framework
title_sort	infer thermal information from visual information: a cross imaging modality edge learning (cimel) framework
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7b02ecd9a61a4b4a9a3b12daa3483b8f
work_keys_str_mv	AT shuozhiwang inferthermalinformationfromvisualinformationacrossimagingmodalityedgelearningcimelframework AT jianqiangmei inferthermalinformationfromvisualinformationacrossimagingmodalityedgelearningcimelframework AT lichaoyang inferthermalinformationfromvisualinformationacrossimagingmodalityedgelearningcimelframework AT yifanzhao inferthermalinformationfromvisualinformationacrossimagingmodalityedgelearningcimelframework
_version_	1718410532245995520

Infer Thermal Information from Visual Information: A Cross Imaging Modality Edge Learning (CIMEL) Framework

Ejemplares similares