Utilizing Deep Convolutional Neural Networks and Non-Negative Matrix Factorization for Multi-Modal Image Fusion Article Swipe
Nripendra Narayan Das
,
G. Santhakumar
,
Sanjiv Rao Godla
,
Yousef A. Baker El–Ebiary
,
E. Thenmozhi
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2023.0140963
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2023.0140963
A key element of contemporary computer vision, image fusion tries to improve the quality and interpretability of images by combining complimentary data from several image sources or modalities. This paper offers a unique method for multi-modal image fusion, combining the benefits of Deep Convolutional Neural Networks (CNNs) and Non-Negative Matrix Factorization (NMF), by using current developments in deep learning and matrix factorization techniques. Deep CNNs have shown to be remarkably effective in extracting features from images, capturing complex patterns and discriminative data. A group of deep CNNs are trained using this suggested technique on a varied dataset of multi-modal images. With the help of these networks, which extract and encode pertinent characteristics from several modalities, information-rich representations may then be combined. Concatenating, the features that were derived from the CNNs throughout the fusion process results in a fused feature representation that perfectly expresses the input modalities. The main novelty is the two-stage integration of NMF: first, breaking down the fused feature representation into non-negative basis vectors and coefficients, and then, using NMF to further extract important patterns from the fused feature maps. The non-negativity requirement in NMF guarantees the preservation of the natural structures and characteristics present in the source images, resulting in fused images that are both aesthetically pleasing and semantically intelligible. Visual examination of the merged images demonstrates the method's capacity to successfully extract important information from several modalities. The better performance and robustness of the suggested approach, which has an accuracy of roughly 99.12%, are highlighted by comparison with existing fusion approaches.
Related Topics
Concepts
Computer science
Interpretability
Artificial intelligence
Convolutional neural network
Pattern recognition (psychology)
Non-negative matrix factorization
Discriminative model
Matrix decomposition
Deep learning
Robustness (evolution)
Feature (linguistics)
Image fusion
Image (mathematics)
Physics
Quantum mechanics
Linguistics
Philosophy
Chemistry
Eigenvalues and eigenvectors
Gene
Biochemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.14569/ijacsa.2023.0140963
- http://thesai.org/Downloads/Volume14No9/Paper_63-Utilizing_Deep_Convolutional_Neural_Network.pdf
- OA Status
- diamond
- Cited By
- 3
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387384977
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387384977Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.14569/ijacsa.2023.0140963Digital Object Identifier
- Title
-
Utilizing Deep Convolutional Neural Networks and Non-Negative Matrix Factorization for Multi-Modal Image FusionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Nripendra Narayan Das, G. Santhakumar, Sanjiv Rao Godla, Yousef A. Baker El–Ebiary, E. ThenmozhiList of authors in order
- Landing page
-
https://doi.org/10.14569/ijacsa.2023.0140963Publisher landing page
- PDF URL
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https://thesai.org/Downloads/Volume14No9/Paper_63-Utilizing_Deep_Convolutional_Neural_Network.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://thesai.org/Downloads/Volume14No9/Paper_63-Utilizing_Deep_Convolutional_Neural_Network.pdfDirect OA link when available
- Concepts
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Computer science, Interpretability, Artificial intelligence, Convolutional neural network, Pattern recognition (psychology), Non-negative matrix factorization, Discriminative model, Matrix decomposition, Deep learning, Robustness (evolution), Feature (linguistics), Image fusion, Image (mathematics), Physics, Quantum mechanics, Linguistics, Philosophy, Chemistry, Eigenvalues and eigenvectors, Gene, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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