Adaptive Scene-Based Non-Uniformity Correction Method for Infrared-Focal Plane Arrays

Sergio N. Torres, Esteban M. Vera, Rodrigo A. Reeves, Sergio K. Sobarzo

Research output: Contribution to journalConference articlepeer-review

86 Scopus citations

Abstract

The non-uniform response in infrared focal plane array (IRFPA) detectors produces corrupted images with a fixed-pattern noise. In this paper we present an enhanced adaptive scene-based non-uniformity correction (NUC) technique. The method simultaneously estimates detector's parameters and performs the non-uniformity compensation using a neural network approach. In addition, the proposed method doesn't make any assumption on the kind or amount of non-uniformity presented on the raw data. The strength and robustness of the proposed method relies in avoiding the presence of ghosting artifacts through the use of optimization techniques in the parameter estimation learning process, such as: momentum, regularization, and adaptive learning rate. The proposed method has been tested with video sequences of simulated and real infrared data taken with an InSb IRFPA, reaching high correction levels, reducing the fixed pattern noise, decreasing the ghosting, and obtaining an effective frame by frame adaptive estimation of each detector's gain and offset.

Original languageEnglish
Pages (from-to)130-139
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5076
DOIs
StatePublished - 2003
Externally publishedYes
EventInfrared Imaging Systems: Design, Analysis Modeling, and Testing XIV - Orlando, FL, United States
Duration: 23 Apr 200324 Apr 2003

Keywords

  • Fixed-Pattern Noise
  • Focal-Plane Array
  • Neural Networks
  • Non-Uniformity Correction

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