Fast adaptive nonuniformity correction for infrared focal-plane array detectors

Esteban Vera, Sergio Torres

Research output: Contribution to journalArticlepeer-review

71 Scopus citations


A novel adaptive scene-based nonuniformity correction technique is presented. The technique simultaneously estimates detector parameters and performs the nonuniformity correction based on the retina-like neural network approach. The proposed method includes the use of an adaptive learning rate rule in the gain and offset parameter estimation process. This learning rate rule, together with a reduction in the averaging window size used for the parameter estimation, may provide an efficient implementation that should increase the original method's scene-based ability to estimate the fixed-pattern noise. The performance of the proposed algorithm is then evaluated with infrared image sequences with simulated and real fixed-pattern noise. The results show a significative faster and more reliable fixed-pattern noise reduction, tracking the parameters drift, and presenting a good adaptability to scene changes and nonuniformity conditions.

Original languageEnglish
Pages (from-to)1994-2004
Number of pages11
JournalEurasip Journal on Applied Signal Processing
Issue number13
StatePublished - 1 Aug 2005
Externally publishedYes


  • Fixed-pattern noise
  • Focal-plane array
  • Infrared detectors
  • Least mean square
  • Neural networks
  • Nonuniformity correction


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