Infer thermal information from visual information: a cross imaging modality edge learning (CIMEL) framework

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dc.contributor.authorWang, Shuozhi
dc.contributor.authorMei, Jianqiang
dc.contributor.authorYang, Lichao
dc.contributor.authorZhao, Yifan
dc.date.accessioned2021-11-19T12:20:16Z
dc.date.available2021-11-19T12:20:16Z
dc.date.issued2021-11-10
dc.description.abstractThe 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 thermographyen_UK
dc.identifier.citationWang S, Mei J, Yang L, Zhao Y. (2021) Infer thermal information from visual information: a cross imaging modality edge learning (CIMEL) framework. Sensors, Volume 21, November 2021, Article number 7471en_UK
dc.identifier.issn1424-8220
dc.identifier.urihttps://doi.org/10.3390/s21227471
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17286
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectimage enhancementen_UK
dc.subjectedge detectionen_UK
dc.subjectdeep learningen_UK
dc.subjectthermographyen_UK
dc.titleInfer thermal information from visual information: a cross imaging modality edge learning (CIMEL) frameworken_UK
dc.typeArticleen_UK

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