Minimum Variance Gain Nonuniformity Estimation in Infrared Focal Plane Array Sensors

  • César San-Martin
  • Gabriel Hermosilla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)


In this paper, a minimum variance estimator for the gain nonuniformity (NU) in infrared (IR) focal plane array (FPA) imaging system is presented. Recently, we have developed a recursive filter estimator for the offset NU using only the collected scene data, assuming that the offset is a constant in a block of frames where it is estimated. The principal assumption of this scene-based NU correction (NUC) method is that the gain NU is a known constant and does not vary in time. However, in several FPA real systems the gain NU drift is significant. For this reason, in this work we present a gain NU drift estimation based on the offset NU recursive estimation assuming that gain and offset are jointly distributed. The efficacy of this NUC technique is demonstrated by employing several real infrared video se quences.


Minimum Variance Estimator Image Sequence Processing Infrared Focal Plane Arrays Signal Processing 


  1. 1.
    Milton, A., Barone, F., Kruer, M.: Influence of nonuniformity on infrared focal plane array performance. Optical Engineering 24, 855–862 (1985)Google Scholar
  2. 2.
    Mooney, J., Shepherd, F., Ewing, W., Murguia, J., Silverman, J.: Responsivity nonuniformity limited performance of infrared staring cameras. Optical Engineering 28, 1151–1161 (1989)Google Scholar
  3. 3.
    Harris, J., Chiang, Y.: Nonuniformity correction of infrared image sequences using constant statistics constraint. IEEE Trans. on Image Processing 8, 1148–1151 (1999)CrossRefGoogle Scholar
  4. 4.
    Hayat, M., Torres, S., Amstrong, E., Cain, S., Yasuda, B.: Statistical algorithm fo nonuniformity correction in focal plane arrays. Applied Optics 38, 773–780 (1999)CrossRefGoogle Scholar
  5. 5.
    Averbuch, A., Liron, G., Bobrovsky, B.: Scene based non-uniformity correction in thermal images using Kalman filter. Image and Vision Computing 25, 833–851 (2007)CrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    Martin, C.S., Torres, S., Pezoa, J.E.: Statistical recursive filtering for offset nonuniformity estimation in infrared focal-plane-array sensors, in press Infrared Physics & Technology (2008)Google Scholar
  11. 11.
    Poor, H.V.: An introduction to signal detection and estimation, 2nd edn. Springer, New York (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • César San-Martin
    • 1
  • Gabriel Hermosilla
    • 2
  1. 1.Information Processing Laboratory, Department of Electrical EngineeringUniversidad de La FronteraTemucoChile
  2. 2.Department of Electrical Eng.Universidad de ChileSantiagoChile

Personalised recommendations