Restoration and range performance in FLIR imaging systems

Image restoration of forward-looking infrared (FLIR) imagery has the potential to significantly improve the quality of images used by an automatic target recognizer (ATR) or human observer. This study investigates the feasibility of real-time image restoration algorithms and the problem of measuring image quality as it relates to target acquisition performance. This paper describes a technique for deriving small kernel filters that efficiently restore and reconstruct. Subject to implementation constraints associated with efficient application, the filters optimize image fidelity to an 'ideal' close-range image. The paper describes simulation experiments employing an end-to-end imaging system model, experiments with actual images using a model-based characterization of an actual imaging system, and simulation experiments that illustrate the utility of the system model and filtering in FLIR imaging system design.