Improved neural network based scene-adaptive nonuniformity correction method for infrared focal plane arrays
Applied Optics, Vol. 47, Issue 24, pp. 4331-4335 (2008)
http://dx.doi.org/10.1364/AO.47.004331
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Abstract
An improved scene-adaptive nonuniformity correction (NUC) algorithm for infrared focal plane arrays (IRFPAs) is proposed. This method simultaneously estimates the infrared detectors’ parameters and eliminates the nonuniformity causing fixed pattern noise (FPN) by using a neural network (NN) approach. In the learning process of neuron parameter estimation, the traditional LMS algorithm is substituted with the newly presented variable step size (VSS) normalized least-mean square (NLMS) based adaptive filtering algorithm, which yields faster convergence, smaller misadjustment, and lower computational cost. In addition, a new NN structure is designed to estimate the desired target value, which promotes the calibration precision considerably. The proposed NUC method reaches high correction performance, which is validated by the experimental results quantitatively tested with a simulative testing sequence and a real infrared image sequence.
© 2008 Optical Society of America
OCIS Codes
(040.1520) Detectors : CCD, charge-coupled device
(100.2000) Image processing : Digital image processing
(100.2550) Image processing : Focal-plane-array image processors
(110.3080) Imaging systems : Infrared imaging
ToC Category:
Image Processing
History
Original Manuscript: January 31, 2008
Revised Manuscript: July 9, 2008
Manuscript Accepted: July 16, 2008
Published: August 14, 2008
Citation
Lai Rui, Yang Yin-tang, Zhou Duan, and Li Yue-jin, "Improved neural network based scene-adaptive nonuniformity correction method for infrared focal plane arrays," Appl. Opt. 47, 4331-4335 (2008)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-47-24-4331
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