Abstract
Image fusion is to converge Multispectral (MS) and Panchromatic (PAN) images into a fused image which is further enlightened. Soft computing based image fusion techniques are fuzzy and neuro-fuzzy are exploited to lessening the severance and vagueness in the output. Fused images achieved after the synthesis is utilized in image analysis, medical applications, armed province, and computer revelation. In this research, we convey iterative image fusion based on fuzzy and neuro-fuzzy methods on source images attained from different sources to improve visualization proficiency. We also compared the proposed techniques with principal component analysis (PCA) and wavelets transform based image fusion. Fused outcomes accomplished from image fusion methods are assessed through typical eminence evaluation parameters. The resulting outcome obtained from iterative fusion is improved in terms of spectral and spatial information when compared to the one-time fused image. Due to neural networks structure, applied sorts of biological neural networks and potentiality of the fuzzy and neuro-fuzzy logic, the proposed method overtakes the conventional methods. The complete investigational consequences formed from anticipated methodology established that the utilization of proposed approaches enhanced image content.
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