<title>Adaptive nonuniformity correction for IR focal-plane arrays using neural networks</title>

Abstract

With rapid advancements in infrared focal plane array (IRFPA) technology, greater demands are being placed on nonuniformity correction (NUC) techniques to provide near-BLIP performance over a wide dynamic range. Standard NUC techniques involve calibrating each detector using reference temperature sources before imaging the IRFPA. Usually the correction needs to be re-calibrated after a short period of time due to IRFPA drift or to adjust for changes in the level of background flux. Adaptive NUC techniques eliminate the need for calibration by continuously updating the correction coefficients based on radiance levels of the scene being viewed. In this manner, continuous compensation can be applied adaptively for individual detector non-idealities and background changes. Two adaptive NUC techniques are discussed; one is a temporal highpass filter and the other involves a neural network with lateral interconnects to nearest neighbor pixels. Both have similarities to biological retinal processing. Questions of implementation and stability are discussed and performance results are given for several test image sequences which were obtained from an MWIR HgCdTe array and a HIDAD uncooled array. We conclude that adaptive techniques will be very useful in future IRFPA sensors, primarily because of their ability to adapt over a wide range of background flux without calibration sources, but also because they can offer improved sensitivity under most operating conditions.