Abstract
In this paper we propose a novel pyramid decomposition based Image fusion metric, Gamma Factor or Goodness of Fit ‘ᴦ’ which describes the statistically amount of information fused by the image fusion algorithm. We first apply steerable pyramid decomposition and then a fitting model for Univariate Generalised Gaussian Distribution (UGGD) parameter estimation. From the UGGD; P and S fitting model coefficients are computed. To estimate the optimum weights for computation a huge data set of complimentary images are used. Using these weights, amount of information contributed by each image to form a fused image can be estimated. Experimental results show the tremendous matching with the quantise information
Highlights
THE objective of image fusion is to coordinate data from different info images to make a combined one that is progressively instructive for human or machine observation as contrasted with any of the information images [1]
Image fusion procedures have been utilized in different application zones counting remote detecting, biomedical imaging, and multi-exposure multi-center image coordination[2]
A significant option in contrast to abstract assessment is objective image fusion estimates that are steady with human visual system
Summary
THE objective of image fusion is to coordinate data from different info images to make a combined one that is progressively instructive for human or machine observation as contrasted with any of the information images [1]. Image fusion procedures have been utilized in different application zones counting remote detecting, biomedical imaging, and multi-exposure multi-center image coordination[2]. Diverse imaging modalities are corresponding in nature in securing unique parts of organic structures and exercises.
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