Image Fusion Using Incremental Higher Order Singular Value Decomposition Method

Authors

  • Thakur I Chandigarh Engineering College, Landran, Mohali (Punjab)
  • Saini H Chandigarh Engineering College, Landran, Mohali (Punjab)

Keywords:

Singular Value Decomposition, Tensors, Image Fusion, Incremental HOSVD, Reduced HOSVD

Abstract

In this paper, we have implemented singular value decomposition to effectively update the value of decomposition, including the basis images. In this paper two dimensional incremental higher order singular value decomposition (HOSVD) is used for image fusion. Incremental higher order SVD will help us to store the images with less storage requirements and will keep the level of the error that must be acceptable in an application. The prime methods used here are HOSVD and its repetitive application. It is already known that singular value matrix obtained by SVD contains the illumination information. Therefore, we will combine this matrix for two different images. Large number of the variations made to this matrix will not affect the other attributes of the image. The incremental approach will be used to divide the image into sub-bands. When the images are separated on LH, HL and HH sub-bands, the effect of fusion will be smoothened by this method.

References

Andras Rovid, Laszlo Szeidl and Peter Varlaki, “The SVD and DWT Based Domain and the Related Image Processing Techniques.” INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND INFORMATICS, Volume 5, Issue 3, 2011

Gagandeep Kour and Sharad P. Singh, “Low Quality Image Information Enhancement Using SVD Fusion Technique.” International Journal Of Engineering And Computer Science ISSN:2319-7242, Volume 2, Issue 11, November 2013, Page No. 3227-3231

P.Ambika Priyadharsini, M.R.Mahalakshmi, “Multimodal Medical Image Fusion Based On SVD”, IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727, Volume 16, Issue 1, Ver. III (Jan. 2014), PP 27-31

Asha P Kurian, Bijitha S R, Lekshmi Mohan, Megha M Kartha, K P Soman, “Performance Evaluation of Modified SVD based Image Fusion,” International Journal of Computer Applications (0975 – 8887) Volume 58– No.12, November 2012.

Michael Thomason and Jens Gregor, “Fusion of Multiple Images by Higher-Order SVD of Third-Order Image Tensors,” 2007.

Hatte S.C., Shingate V.S, “INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY,” International Journal of Engineering Sciences & Research Technology.

Berkant Savas, Lars Eldén, “Handwritten digit classification using higher order singular value decomposition.”

Piyu Tsai, yu-chen Hu, Hsiu-Lien Yeh. Reversible image hiding scheme using predictive coding and histogram shifting. Signal processing. 2009; 89(6): 1129-1143.

Nadia Kreimer and Mauricio D. Sacchi, “A tensor higher-order singular value decomposition (SVD AND DWT) for pre-stack seismic data noise-reduction and interpolation,” 2012.

Badrul Sarwar, George Karypis, Joseph Konstan and John Riedl, “Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems.”

M. Gonzalez-Audicana, J.L. Saleta, R.G. Catalan, and R. Garcia, “Fusion of Multispectral and Panchromatic Images Using Improved IHS and PCA Mergers Based on Wavelet Decomposition,” IEEE Trans. Geoscience and Rem. Sen., vol. 42, pp. 1291-1299, 2004.

A. A. Goshtasby, “Fusion of Multi-Exposure Images,” Image and Vis. Comp., vol. 23, pp. 611-618, 2005.

A. A. Goshtasby and S. Nikolov, “Image Fusion: Advances in the State of the Art,” Info. Fusion, vol. 8, pp. 114-118, 2007.

M.T. Heath, Scientific Computing: An Introductory Survey, Second Ed., McGraw-Hill, NY, 2002.

H. A. L. Kiers, “Towards a Standardized Notation and Terminology in Multiway Analysis,” J. Chemometrics, vol. 14, pp. 105-122, 2000

Downloads

Published

2014-12-06

How to Cite

[1]
I. Thakur and H. Saini, “Image Fusion Using Incremental Higher Order Singular Value Decomposition Method”, Int. J. Comp. Sci. Eng., vol. 2, no. 11, pp. 47–50, Dec. 2014.

Issue

Vol. 2 No. 11 (2014): IJCSE November Edition

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.