Open AccessStatistical Goodness Factor ‘ᴦ’ for Image Fusion Algorithm Based on UGGD Parameters
Author(s) -
Msa. Srivatsava,
T. Ramashri,
K. Soundararajan
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c6399.029320
Subject(s) - goodness of fit , metric (unit) , univariate , image fusion , computation , pyramid (geometry) , artificial intelligence , image (mathematics) , algorithm , set (abstract data type) , pattern recognition (psychology) , matching (statistics) , gaussian , computer science , mathematics , decomposition , data set , statistics , multivariate statistics , ecology , operations management , geometry , physics , quantum mechanics , economics , biology , programming language
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