Infrared sensor performance with boost and restoration filtering.

Abstract

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