Quality Assessment Parameters for Iterative Image Fusion Using Fuzzy and Neuro Fuzzy Logic and Applications. (2015)

• Record Type: Journal Article
• Authors: Rao, D. Srinivasa; Seetha, M.; Prasad, M.H.M. Krishna
• Is Part Of: Procedia technology. Volume 19(2015)
• Page Start: 888
• Page End: 894
• Publication Date: 2015
• Subjects: fusion factor -- fusion index -- fuzzy logic -- fusion symmetry -- image quality inde -- mutual information measure -- neuro fuzzy logic -- peak signal to noise rati -- root mean square error -- spatial frequency.; Technology -- Congresses; Technology -- Periodicals; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Technology; Conference proceedings; Periodicals; 605
• DOI: 10.1016/j.protcy.2015.02.127
• Languages: English
• ISSNs: 2212-0173

Abstract

Abstract: Image fusion is a process to reduce ambiguity and diffusion while retrieving the valuable information from the original images. Image fusion is demanded for different contexts like remote sensing, medical imaging, machine vision and biometrics. In this paper, an iterative image fusion using fuzzy and neuro fuzzy logic approaches are used to fuse images taken from different sensors to enhance the perception. The proposed work also explores comparison among fuzzy based image fusion, iterative fuzzy fusion, neuro fuzzy logic based image fusion and iterative image fusion using neuro fuzzy logic techniques through quality assessment metrics for image fusion like image quality index, mutual information measure, fusion factor, fusion symmetry, fusion index, root mean square error, peak signal to noise ratio and spatial frequency. Experimental outcomes attained from proposed method prove that the use of the iterative fuzzy and iterative neuro fuzzy fusion can efficiently retain the illusory information while increasing the spatial information of the remote sensing and medical imaging.