This Small Business Innovative Research (SBIR) Phase I project aims to design, test and implement a novel scene-based nonuniformity correction (SBNUC) algorithm for use in microbolometer-based uncooled thermal imagers. The approach relies on exploiting telescopic motion in the scene, in a video sequence, inherent in imagery acquired by a camera that is mounted in the front of an operating vehicle, to algebraically extract the nonuniformity-noise parameters in a dynamic fashion, without the need for the usual shutter-based calibration. The technology offers a real-time solution to nonuniformity correction for thermal imagers in automobiles while the camera is still imaging the scene, without any disruption of its operation. If successful, this approach would enable an alternate method to process changes and to reduce the cost of thermal and other imager technologies.
The diversification of both amorphous-Si based and vanadium oxide (VOx) based microbolometer-detector technologies is currently limited by cost, FPN performance, and mechanical reliability (e.g. shutter). The proposed approach will provide the ability to apply the uncooled cost-effective microbolometer detector to markets where there is a need for a low-cost, shutter-free thermal imagers. The auto industry has already started employing microbolometer-based night-vision systems to improve safety during night driving. This project will also have a direct impact on a broad spectrum of sensing applications including lightweight vehicle or handheld night-vision systems, thermal imaging cameras used in forest-fire detection, and law-enforcement applications.
