Advertisement

A RLS Filter for Nonuniformity and Ghosting Correction of Infrared Image Sequences

  • Flavio Torres
  • César San Martin
  • Sergio N. Torres
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)

Abstract

In this paper, a technique to improve the convergence and to reduce the ghosting artifacts of a previously developed adaptive scene-based nonuniformity correction method is presented. The nonuniformity correction method estimates detector parameters based on the recursive least square filter approach. We propose, three parameters to reduce ghosting artifacts and to speed up the convergence of such method by using only the read-out data. The parameters proposed are based in identify global motion between consecutive frames as well as evaluate the main assumption used in the previous method in the uncertainty on the input infrared irradiance. 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 Imaging RLS 

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. Proceeding of SPIE 1541, 100–109 (1991)CrossRefGoogle 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. Proceeding of SPIE 5076, 130–139 (2003)CrossRefGoogle Scholar
  6. 6.
    Vera, E., Torres, S.: Fast Adaptive Nonuniformity Correction for Infrared Focal Plane Arrays. EURASIP Journal on Applied Signal Processing (2005)Google Scholar
  7. 7.
    Torres, F., Torres, S., San Martin, C.: A Recursive Least Square Adaptive Filter for Nonuniformity Correction of Infrared Image Sequences. In: Sanfeliu, A., Cortés, M.L. (eds.) CIARP 2005. LNCS, vol. 3773, pp. 540–546. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Ljung, L., Söderström, T.: Theory and practice of recursive identification. MIT Press, Cambridge (1983)zbMATHGoogle Scholar
  9. 9.
    Eleftheriou, E., Falconer, D.D.: Tracking properties and steady-state performance of RLS adaptive filter algorithms. IEEE Trans. Acoust. Speech Signal Process. ASSP 34, 1097–1110 (1986)CrossRefGoogle Scholar
  10. 10.
    Ewada, E.: Comparasion of RLS, LMS and sign algorithms for tracking randomly time-varying channels, IEEE Trans. IEEE Trans. Signal Process 42, 2937–2944 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Flavio Torres
    • 1
  • César San Martin
    • 1
    • 2
  • Sergio N. Torres
    • 2
  1. 1.Department of Electrical EngineeringUniversity of La FronteraTemucoChile
  2. 2.Department of Electrical EngineeringUniversity of ConcepciónConcepciónChile

Personalised recommendations