Study of Intelligence and Enhanced Techniques of Data Fusion using Constitutional Neural Networks (CNNs) and Principal Component Analysis (PCA)

Authors

  • Sunil Gupta Computer Science & Engineering, Chitkara University Institute of Engineering & Technology, Chitkara University, Rajpura, Punjab, India
  • Dunde Venu Kakatiya Institute of Technology and Science, Warangal, Telangana, India.
  • Pallavi Vippagunta Lecturer in Mathematics, Department of Information Technology University of Technology and Applied Sciences, Higher College of Technology, Muscat.
  • Jagadeesh B. N. Assistant Professor, Department of ISE, RNS Institute of Technology, Bangalore, India.
  • Banda Saisandeep Assistant Professor, Department of ECE, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India.
  • Laith Abualigah omputer Science Department, Al al-Bayt University, Mafraq 25113, Jordan.

Keywords:

CNN, Image Fusion, IQA, Multi-focus image fusion, Mean Square Error, PCA, PSNR, SNR

Abstract

Image fusion refers to a wide class of data processing methods that attempt to pool information from many images taken with the same or different spectroscopic instruments or on different platforms. Both regression models that link seemingly unrelated photos or the construction of a single multiset or multiway structure can be used for image fusion, data analysis based on the structures of fused images always trumps the results of individual picture analysis.Image fusion can be employed in many different situations and for many different reasons, such as the characterization of components in 3D hyperspectral images or in sets of linked 2D photos. An image is a representation of a real-world item or person created by optical (such as a lens or mirror) or electronic means.In this study, we examine the use of principal component analysis with convolution neural networks for the purpose of multi-resolution picture fusion.in this research reveals that PCA and CNN both have their benefits and drawbacks. Despite being the most elementary approach to image fusion, principal component analysis (PCA) has been shown to be the least efficient in our studies. Convolutional neural networks (CNNs), on the other hand, are effective but challenging to maintain and may produce merged images with mismatched, unrecognizable boundary pixels.

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Published

24.03.2024

How to Cite

Gupta, S. ., Venu, D. ., Vippagunta, P. ., B. N., J. ., Saisandeep, B. ., & Abualigah, L. . (2024). Study of Intelligence and Enhanced Techniques of Data Fusion using Constitutional Neural Networks (CNNs) and Principal Component Analysis (PCA). International Journal of Intelligent Systems and Applications in Engineering, 12(20s), 503–512. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5163

Issue

Vol. 12 No. 20s (2024)

Section

Research Article

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