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A Neural Network for Nonuniformity and Ghosting Correction of Infrared Image Sequences

  • Sergio N. Torres
  • Cesar San Martin
  • Daniel G. Sbarbaro
  • Jorge E. Pezoa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3656)

Abstract

In this paper, an adaptive scene-based nonuniformity and ghosting artifacts correction algorithm for infrared image sequences is presented. The method simultaneously estimates detector parameters and carry out the non-uniformity and ghosting artifacts correction based on the retina-like neural network approach. The method incorporates the use of a new adaptive learning rate rule into the estimation of the gain and the offset of each detector. This learning rule, together with the consideration of the dependence of the detector’s parameters on the retinomorphic assumption used for parameter estimation, may sustain an efficient method that could not only increase the original method’s ability for estimating the non-uniformity noise, but also increase the capability of mitigating ghosting artifacts. The ability of the method to compensate for nonuniformity and reducing ghosting artifacts is demonstrated by employing several infrared video sequences obtained using two infrared cameras.

Keywords

Image Sequence Processing Infrared Focal Plane Arrays Neural Network 

Topic

Vision and Image Processing Signal Processing 

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References

  1. 1.
    Torres, S., Hayat, M.: Kalman Filtering for Adaptive Nonuniformity Correction in Infrared Focal Plane Arrays. The JOSA-A Opt. Soc. of America 20, 470–480 (2003)CrossRefGoogle Scholar
  2. 2.
    Torres, S., Pezoa, J., Hayat, M.: Scene-based Nonuniformity Correction for Focal Plane Arrays Using the Method of the Inverse Covariance Form. OSA App. Opt. Inf. Proc. 42, 5872–5881 (2003)Google Scholar
  3. 3.
    Scribner, D., Sarkady, K., Kruer, M.: Adaptive Nonuniformity Correction for Infrared Focal Plane Arrays using Neural Networks. In: Proceeding of SPIE, vol. 1541, pp. 100–109 (1991)Google Scholar
  4. 4.
    Scribner, D., Sarkady, K., Kruer, M.: Adaptive Retina-like Preprocessing for Imaging Detector Arrays. In: Proceeding of the IEEE International Conference on Neural Networks, vol. 3, pp. 1955–1960 (1993)Google Scholar
  5. 5.
    Torres, S., Vera, E., Reeves, R., Sobarzo, S.: Adaptive Scene-Based Nonuniformity Correction Method for Infrared Focal Plane Arrays. In: Proceeding of SPIE, vol. 5076, pp. 130–139 (2003)Google Scholar
  6. 6.
    Vera, E., Torres, S.: Fast Adaptive Nonuniformity Correction for Infrared Focal Plane Arrays. To be published in EURASIP Journal on Applied Signal Processing (2005)Google Scholar
  7. 7.
    Vijaya Kumar, B.V.K.: Tutorial survey of composite filter designs for optical correlators. Appl. Opt. 31, 4774–4801 (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sergio N. Torres
    • 1
  • Cesar San Martin
    • 1
  • Daniel G. Sbarbaro
    • 1
  • Jorge E. Pezoa
    • 1
  1. 1.Department of Electrical EngineeringUniversity of ConcepciónConcepciónChile

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