國立虎尾科技大學 |

Non-Uniformity Correction Using Nonlinear Characteristic Performance Curves for Calibration.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Non-Uniformity Correction Using Nonlinear Characteristic Performance Curves for Calibration./
作者:	Lovejoy, McKenna Roberts.
面頁冊數:	1 online resource (169 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355518047

Non-Uniformity Correction Using Nonlinear Characteristic Performance Curves for Calibration.
Lovejoy, McKenna Roberts.

Non-Uniformity Correction Using Nonlinear Characteristic Performance Curves for Calibration. - 1 online resource (169 pages)

Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.

Thesis (Ph.D.)--University of Colorado Colorado Springs, 2017.

Includes bibliographical references

Infrared imaging is an expansive field with many applications. Advances in infrared technology have lead to a greater demand from both commercial and military sectors. However, a known problem with infrared imaging is its non-uniformity. This non-uniformity stems from the fact that each pixel in an infrared focal plane array has its own photoresponse. Many factors such as exposure time, temperature, and amplifier choice affect how the pixels respond to incoming illumination and thus impact image uniformity. To improve performance non-uniformity correction (NUC) techniques are applied. Standard calibration based techniques commonly use a linear model to approximate the nonlinear response. This often leaves unacceptable levels of residual non-uniformity. Calibration techniques often have to be repeated during use to continually correct the image.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355518047Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Non-Uniformity Correction Using Nonlinear Characteristic Performance Curves for Calibration.
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245 1 0 $a Non-Uniformity Correction Using Nonlinear Characteristic Performance Curves for Calibration.
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500 $a Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.
500 $a Adviser: Mark A. Wickert.
502 $a Thesis (Ph.D.)--University of Colorado Colorado Springs, 2017.
504 $a Includes bibliographical references
520 $a Infrared imaging is an expansive field with many applications. Advances in infrared technology have lead to a greater demand from both commercial and military sectors. However, a known problem with infrared imaging is its non-uniformity. This non-uniformity stems from the fact that each pixel in an infrared focal plane array has its own photoresponse. Many factors such as exposure time, temperature, and amplifier choice affect how the pixels respond to incoming illumination and thus impact image uniformity. To improve performance non-uniformity correction (NUC) techniques are applied. Standard calibration based techniques commonly use a linear model to approximate the nonlinear response. This often leaves unacceptable levels of residual non-uniformity. Calibration techniques often have to be repeated during use to continually correct the image.
520 $a In this dissertation alternates to linear NUC algorithms are investigated. The goal of this dissertation is to determine and compare nonlinear non-uniformity correction algorithms. Ideally the results will provide better NUC performance resulting in less residual non-uniformity as well as reduce the need for recalibration. This dissertation will consider new approaches to nonlinear NUC such as higher order polynomials and exponentials. More specifically, a new gain equalization algorithm has been developed. The various nonlinear non-uniformity correction algorithms will be compared with common linear non-uniformity correction algorithms. Performance will be compared based on RMS errors, residual non-uniformity, and the impact quantization has on correction.
520 $a Performance will be improved by identifying and replacing bad pixels prior to correction. Two bad pixel identification and replacement techniques will be investigated and compared. Performance will be presented in the form of simulation results as well as before and after images taken with short wave infrared cameras. The initial results show, using a third order polynomial with 16-bit precision, significant improvement over the one and two-point correction algorithms. All algorithm have been implemented in software with satisfactory results and the third order gain equalization non-uniformity correction algorithm has been implemented in hardware.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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