Infrared sensor performance with boost and restorationfiltering

摘要: The targeting task performance (TTP) model for prediction of target identification range suggests that boost filtering with a well-sampled, low-noise long-wave infrared (LWIR) sensor can substantially increase target ID (by enhancing contrast at high spatial frequencies). We model notional high-performance LWIR imaging system high F-number, deep electron wells, and small-pitch focal plane array. System analysis performed the Night Vision Integrated Performance Model (NVIPM) predicts enhancement upwards 50% is achievable with Wiener restoration applied to imagery from modeled sensor. Human perception experiments were on simulated target imagery, with range through different filters (including restoration filter) compared no-post-filter case. TTP was found to significantly overestimate improvement due and restoration filtering. Alternate predictions based Johnson criteria were also performed, these underestimated impact boost. We speculate reasons discrepancy promising avenues for future research. Sensor parameters, NVIPM predictions, filter parameters, and experimental data are provided.