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Image Fusion on Wavelet Probability Statistic

Liu Weiguang, Zho LihuaInstitute of Multimedia Technology, Xidian University, Xi′an 710071, China]  
A probabilistic method for fusion based on wavelet is presented. A Bayesian framework provides for maximum likelihood or maximum a posterior estimates of the true scene from the sensor images .This estimate constitutes the rule for fusing the sensor images. Utilizing wavelet decomposition images into multiple levels, at which approximate the map between the true scene and the sensors by a local affine transformation is defined.A Maximum Likelihood Estimates (MLE) for the parameters of the local affine transformations is given. The merging rule is derived under the assumption of local affine transformation. The fusion results of visible image and infrared image obtained on aerophotography can be performed under local polarity reversals perfectly by our scheme. Experiments and performance show that the method is valid .Abstract A probabilistic method for fusion based on wavelet is presented. A Bayesian framework provides for maximum likelihood or maximum a posterior estimates of the true scene from the sensor images .This estimate constitutes the rule for fusing the sensor images. Utilizing wavelet decomposition images into multiple levels, at which approximate the map between the true scene and the sensors by a local affine transformation is defined.A Maximum Likelihood Estimates (MLE) for the parameters of the local affine transformations is given. The merging rule is derived under the assumption of local affine transformation. The fusion results of visible image and infrared image obtained on aerophotography can be performed under local polarity reversals perfectly by our scheme. Experiments and performance show that the method is valid .
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