Image fusion using the expectation-maximization algorithm and a hidden Markov model

A statistical signal processing approach to multisensor image fusion is presented. This approach is based on an image formation model in which the sensor images are described as the true scene corrupted by additive non-Gaussian distortion. A hidden Markov model (HMM) is fitted to the wavelet transforms of the sensor images to describe the correlations between the coefficients across wavelet decomposition scales. A set of iterative equations was developed using the expectation-maximization (EM) algorithm to estimate the model parameters and produce the fused images. We demonstrated the efficiency of this approach by applying this method to visual and radiometric images in concealed weapon detection (CWD) cases and night vision applications.

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