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Infrared sensor performance with boost and restoration filtering.

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    Boost filtering enhances long-wave infrared (LWIR) sensor target identification range. However, the targeting task performance (TTP) model overestimates this improvement, while Johnson criteria underestimate it.

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    Area of Science:

    • Optics and Photonics
    • Image Processing
    • Sensor Technology

    Background:

    • The targeting task performance (TTP) model predicts increased target identification range using boost filtering with long-wave infrared (LWIR) sensors.
    • LWIR sensors enhance contrast at high spatial frequencies, improving target detection.

    Purpose of the Study:

    • To evaluate the effectiveness of boost and restoration filtering on LWIR sensor performance.
    • To compare model predictions (TTP and Johnson criteria) with human perception experimental data.

    Main Methods:

    • Modeling a high-performance LWIR imaging system with specific parameters (high F-number, deep electron wells, small-pitch focal plane array).
    • System analysis using the Night Vision Integrated Performance Model (NVIPM).
    • Human perception experiments with simulated target imagery and various boost filters, including Wiener restoration.

    Main Results:

    • NVIPM predicted over 50% range enhancement with Wiener restoration.
    • Human perception experiments revealed the TTP model significantly overestimated performance gains from filtering.
    • Johnson criteria underestimated the impact of boost filtering.

    Conclusions:

    • The TTP model requires refinement as it overestimates filtering benefits for LWIR target identification.
    • Further research is needed to reconcile model predictions with experimental findings for accurate performance assessment.