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Joint estimation of unknown radiometric data, gain, and offset from thermal images - Abstract
Year:
2018
Source of publication :
Applied Optics
Authors :
Klapp, Iftach
;
.
Papini, Shahar
;
.
Yafin, Peretz 
;
.
Volume :
57
Co-Authors:
Facilitators :
From page:
10390
To page:
10401
(
Total pages:
12
)
Abstract:

Low cost, weight, and size microbolometer-based thermal focal plane arrays are attractive for thermal-imaging applications. Under environmental loads like those in agricultural remote sensing, these cameras tend to suffer from drift in gain and offset with time and thus require constant calibration. Our goal is to skip this step via computational imaging. In a previous work we estimated the unknown offset value and radiometric image of an object, given the calibrated gain, from a pair of successive images taken at two different blur levels, eliminating the need for offset calibration due to temperature variation. Here, we extend our model to a case with unknown gain and offset. We show that these values, as well as the objects’ radiometric value, can be found jointly by minimizing a cost function relying on N pairs of blurred and sharp images. The method addresses both space-invariant and space-variant cases. Simulations show promising accuracy with error characterized by root mean squared error of less than 1.6°C. © 2018 Optical Society of America

Note:
Related Files :
calibration
Infrared imaging
Mean square error
radiometry
remote sensing
Root mean squared errors
Show More
Related Content
More details
DOI :
10.1364/AO.57.010390
Article number:
0
Affiliations:
Database:
Scopus
Publication Type:
Abstract
;
.
article
;
.
Language:
English
Editors' remarks:
ID:
38938
Last updated date:
02/03/2022 17:27
Creation date:
08/01/2019 14:55
Scientific Publication
Joint estimation of unknown radiometric data, gain, and offset from thermal images - Abstract
57
Joint estimation of unknown radiometric data, gain, and offset from thermal images

Low cost, weight, and size microbolometer-based thermal focal plane arrays are attractive for thermal-imaging applications. Under environmental loads like those in agricultural remote sensing, these cameras tend to suffer from drift in gain and offset with time and thus require constant calibration. Our goal is to skip this step via computational imaging. In a previous work we estimated the unknown offset value and radiometric image of an object, given the calibrated gain, from a pair of successive images taken at two different blur levels, eliminating the need for offset calibration due to temperature variation. Here, we extend our model to a case with unknown gain and offset. We show that these values, as well as the objects’ radiometric value, can be found jointly by minimizing a cost function relying on N pairs of blurred and sharp images. The method addresses both space-invariant and space-variant cases. Simulations show promising accuracy with error characterized by root mean squared error of less than 1.6°C. © 2018 Optical Society of America

Scientific Publication
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