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Scene-based nonuniformity correction using local constant statistics.

Chao Zhang1, Wenyi Zhao

  • 1Sarnoff Corporation, Princeton, New Jersey 08540, USA. dzhang@sarnoff.com

Journal of the Optical Society of America. A, Optics, Image Science, and Vision
|June 3, 2008
PubMed
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This study introduces a new statistical method for nonuniformity correction, reducing "ghosting" artifacts by assuming local, not global, constant statistics. This technique effectively separates fixed pattern noise from spatial scene variations for clearer images.

Area of Science:

  • Image Processing
  • Signal Processing
  • Optical Engineering

Background:

  • Scene-based nonuniformity correction (NUC) methods often struggle with "ghosting" artifacts.
  • Existing statistical NUC approaches assume global-constant statistics, conflating fixed pattern noise with spatial scene variations.
  • This leads to inaccuracies where spatial scene variations are misinterpreted as noise.

Purpose of the Study:

  • To introduce a novel statistical method for nonuniformity correction that significantly reduces ghosting artifacts.
  • To differentiate fixed pattern noise from genuine spatial variations in the scene.
  • To improve the accuracy and quality of corrected infrared and CCD video sequences.

Main Methods:

  • Proposed a local-constant statistics approach, assuming signal distribution is constant within local regions but varies on a larger scale.

Related Experiment Videos

  • Utilized a wavelet method to separate fixed pattern noise (higher spatial frequency) from spatial variations (lower spatial frequency).
  • Applied the method to gain and offset images to isolate noise components.

    • Main Results:

    • The new method effectively reduces ghosting artifacts compared to the global-constant-statistics approach.
    • Demonstrated successful separation of fixed pattern noise from spatial scene variations.
    • Achieved improved image quality in both simulated and real-world (CCD and LWIR) sequences.

    Conclusions:

    • The local-constant statistics method offers a significant improvement in nonuniformity correction by mitigating ghosting artifacts.
    • Wavelet-based noise separation is effective in distinguishing fixed pattern noise from scene content.
    • The proposed technique enhances the reliability and visual quality of NUC for various imaging applications.