Bayesian sensor image fusion using local linear generative models

We present a probabilistic method for fusion of images produced by multiple sensors. The approach is based on an image formation model in which the se nsor images are noisy, locally linear functions of an underlying true scene (latent variable). A Bayesian framework then provides for maximum-likeliho od or maximum a posteriori estimates of the true scene from the sensor imag es. Least-squares estimates of the parameters of the image formation model involve (local) second-order image statistics, and are related to local pri ncipal-component analysis. We demonstrate the efficacy of the method on ima ges from visible-band and infrared sensors. (C) 2001 Society of Photo-Optic al Instrumentation Engineers.