Bayesian sensor image fusion using local linear generative models

We present a probabilistic method for fusion of images pro- duced by multiple sensors. The approach is based on an image forma- tion model in which the sensor images are noisy, locally linear functions of an underlying true scene (latent variable). A Bayesian framework then provides for maximum-likelihood or maximum a posteriori estimates of the true scene from the sensor images. Least-squares estimates of the parameters of the image formation model involve (local) second-order image statistics, and are related to local principal-component analysis. We demonstrate the efficacy of the method on images from visible-band and infrared sensors. © 2001 Society of Photo-Optical Instrumentation Engineers. (DOI: 10.1117/1.1384886)

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