MMIF-NET: multi model image fusion using deep learning convolutional neural network

M. Laxman Rao; B. Kiran Babu; A.Venkata Mahesh; M. Rajesh; U. Sirisha

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MMIF-NET: multi model image fusion using deep learning convolutional neural network

Author(s)

M. Laxman Rao Department of Computer Science and Systems Engineering, Andhra University,Visakhapatnam, Andhra Pradesh,India

B. Kiran Babu Department of Electronics and Communication Engineering, Godavari Institute of Engineering and Technology,East Godavari, Andhra Pradesh,India

A.Venkata Mahesh Department of Computer Science and Engineering, GIET UNIVERSITY,Gunupur, Orissa,India

M. Rajesh Department of Technical Education,Government Polytechnic, Hyderabad, Telangana,India

U. Sirisha Department of Computer Science and Engineering, Sri Venkateswara College of Engineering and Technology,Etcherla, Srikakulam,India

Abstract

Image fusion plays a major role in many computer vision applications. However, the conventional image processing methods were failed to perform the fusion operation. Therefore, this work focused on the development of multi model image fusion network (MMIF-Net) using deep learning convolutional neural network (DLCNN). Initially, preprocessing operation is carried out using median filter, which removes the different types of noises from source MRI and CT images. Then, pixel-specific features were extracted using DLCNN model, which performed the feature-specific content-based fusion. Here, the DLCNN is used to extract the probabilities of principal component analysis in each MRI, CT region. Then, the post-processing operation is implemented using Gaussian filter, which enhanced the overall texture, spatial, spectral regions of MRI, CT images. The simulation results show that the proposed method resulted in optimal performance than the conventional image fusion methods.

Publication Details

Page(s) 1149-1156

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 18, Issue: 10, Year: 2023

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Keywords

Gaussian Filter, median filter, deep learning convolutional neural network, multi model image fusion network

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