Kalman filtering for adaptive nonuniformity correction in infrared focal-plane arrays.
J Opt Soc Am A Opt Image Sci Vis
; 20(3): 470-80, 2003 Mar.
Article
em En
| MEDLINE
| ID: mdl-12630832
A novel statistical approach is undertaken for the adaptive estimation of the gain and bias nonuniformity in infrared focal-plane array sensors from scene data. The gain and the bias of each detector are regarded as random state variables modeled by a discrete-time Gauss-Markov process. The proposed Gauss-Markov framework provides a mechanism for capturing the slow and random drift in the fixed-pattern noise as the operational conditions of the sensor vary in time. With a temporal stochastic model for each detector's gain and bias at hand, a Kalman filter is derived that uses scene data, comprising the detector's readout values sampled over a short period of time, to optimally update the detector's gain and bias estimates as these parameters drift. The proposed technique relies on a certain spatiotemporal diversity condition in the data, which is satisfied when all detectors see approximately the same range of temperatures within the periods between successive estimation epochs. The performance of the proposed technique is thoroughly studied, and its utility in mitigating fixed-pattern noise is demonstrated with both real infrared and simulated imagery.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
J Opt Soc Am A Opt Image Sci Vis
Assunto da revista:
OFTALMOLOGIA
Ano de publicação:
2003
Tipo de documento:
Article
País de afiliação:
Chile
País de publicação:
Estados Unidos