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2018 | OriginalPaper | Chapter

Contrast Enhancement Algorithm for IR Thermograms Using Optimal Temperature Thresholding and Contrast Stretching

Authors : Jaspreet Singh, Ajat Shatru Arora

Published in: Advances in Machine Learning and Data Science

Publisher: Springer Singapore

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Abstract

IR thermography is a noninvasive and non-contact type radiometric technique which creates the 2D thermal images based on infrared radiations. Usually, these are gray-level images which provide poor color contrast. However, various pseudo-coloring algorithms are available to transform these images into RGB space, but contrast enhancement is still required for better visualization of thermograms. In this study, the non-training contrast enhancement algorithm is proposed for IR thermograms. The contrast enhancement in this proposed methodology is achieved by: (i) eliminating the background interference using optimal temperature thresholding and (ii) color enhancement using decorrelation contrast stretching. The performance of proposed methodology has been evaluated based on variations in entropy values. The increasing trend in entropy values indicates the contrast enhancement achieved by using this method.

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Metadata
Title
Contrast Enhancement Algorithm for IR Thermograms Using Optimal Temperature Thresholding and Contrast Stretching
Authors
Jaspreet Singh
Ajat Shatru Arora
Copyright Year
2018
Publisher
Springer Singapore
Book
Advances in Machine Learning and Data Science

Print ISBN: 978-981-10-8568-0

Electronic ISBN: 978-981-10-8569-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-8569-7_37

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