MMIF-Net: Multi model image fusion using deep learning convolutional neural network
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Author |
M. Laxman Rao, B. Kiran Babu, A. Venkata Mahesh, M. Rajesh and U. Sirisha
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e-ISSN |
1819-6608 |
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On Pages
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1149-1156
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Volume No. |
18
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Issue No. |
10
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Issue Date |
July 25, 2023
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DOI |
https://doi.org/10.59018/0523150
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Keywords |
multi model image fusion network, deep learning convolutional neural network, median filter, gaussian filter.
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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.
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