Statistical Goodness Factor ‘ᴦ’ for Image Fusion Algorithm Based on UGGD Parameters
MSA Srivatsava1, T. Ramashri2, K Soundararajan3
1MSA Srivatsava, Head and Director, Department of ECE, JNTUA College of Engineering, JNTU Anantapur, Anantapuramu.
2Prof. Dr. T. Ramashri, Head, Department of ECE, JNTU College of Engineering, JNTU Anantapur, Anantapuramu.
3Prof. Dr. K. Soundararajan, Professor, Department of ECE, JNTU College of Engineering, JNTU Anantapur, Anantapuramu.
Manuscript received on January 21, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 4297-4299 | Volume-9 Issue-3, February 2020. | Retrieval Number: C6399029320/2020©BEIESP | DOI: 10.35940/ijeat.C6399.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Pyramid, UGGD, Weights, Fitting model, Fused Image.