A Comparative Analysis of Image Fusion Methods Using Texture

  • Jharna Majumdar
  • Bhuvaneshwari S. Patil
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 221)


Image fusion produces a single image from a set of input images such that the fused image have more complete information useful for human or machine perception. In the proposed paper, authors have used feature based image fusion, where textures of the image are used as feature. Image Texture is a process that can be applied to the pixel of an image in order to generate a measure (feature) related to the texture pattern, to which that pixel and its neighbors belong. Authors have used five different texture feature extraction methods for fusion of multi sensor, multi focal, multi temporal and multi spectral imagery. The methods are: GLCM, Runlength, Statistical, Tamura and Texture Spectrum. The performance of fusion algorithm is measured using a number of nonreference quality assessments metric. A meaningful comparison of results and analysis show the suitability of various texture features for fusion of images from multiple modalities.


GLCM Runlength Statistical Tamura and texture spectrum Image fusion Quality parameters 



The authors gratefully acknowledge VTU Research Fund, VTU Belgaum for sponsoring the Project at the Nitte Meenakshi Institute of Technology, Bangalore. The authors gratefully acknowledge Venkatesh G M, Research Associate of NMIT for his contribution during the course of this work. The authors acknowledge the constant support and encouragement provided by Dr. N.R. Shetty, Director NMIT and Dr. H C Nagaraj, Principal NMIT during the course of this work.


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Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Department of CSE(PG)Nitte Institute of TechnologyBangaloreIndia
  2. 2.Department of CSE(PG)Nitte Institute of TechnologyBangaloreIndia

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