Infrared sensor performance with boost and restoration filtering.

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.

Performance benefits of small-pitch, high-dynamic-range, digitally restored infrared imaging systems using triangle orientation discrimination and NVIPM.

For the past year, the authors have been studying a long-wave infrared (LWIR) sensor design concept that combines high detector well capacity, small-pitch detectors, and digital image processing to optimize target acquisition. Theoretical performance modeling [via the Night Vision Integrated Performance Model (NVIPM)] suggests that our approach offers a large increase in target identification range, but multiple field trials using triangle orientation discrimination (TOD) have yielded results that are inconsistent with the model's predictions. For this reason, we have performed human perception experiments on simulated TOD targets, with and without image processing, to assess the utility of our approach and the value of TOD as an evaluation for digital image enhancement.