Image fusion using wavelet transform: Systematic literature review

Sushant Bindra; Mehak Piplani

editorSES@esciencesspectrum.com

Spectrum of Emerging Sciences

ISSN: 2583-2603 | Indexed In: Google Scholar, Researchgate | Issues per year: Bi-annually

ISSN:2583-2603

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Image fusion using wavelet transform: Systematic literature review

Author(s): Sushant Bindra, Mehak Piplani

Email(s): artihadap08@gmail.com

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Published In: Volume - 1, Issue - 1, Year - 2021

DOI: Not Available

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ABSTRACT:
The genesis of an Image fusion is intermingling multiple image of usual features to frame a solitary image which attains all the indispensable characteristics of image. At the moment a great deal of effort is going to be executed on the field of image fusion and used in various applications well like medical imaging and multi spectra sensor image fusing etc. For fusing the image numerous techniques have been proposed like wavelet transform. In present review article the image fusion along with wavelet transform has been discussed with its advantages and disadvantages along with defined parameters like entropy, mutual information, cross entropy, Fusion similarity metric (FSM), etc. It's tough to say which strategy is ideal for a particular application. For the image fusion application, all of the approaches were determined to be reliable.

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Cite this article:
Sushant Bindra, Mehak Piplani (2021). Image Fusion using Wavelet Transform: Systematic Literature Review. Spectrum of Emerging Sciences, 1(1), pp.9-15.

References:

Staniforth S, Wallert A, Hermens E, Peek M, Hermens E. Historical Painting Techniques, Materials, and Studio Practice. Studies in Conservation. 1999;44(2):142.
Bai X, Zhang Y, Zhou F, Xue B. Quadtree-based multi-focus image fusion using a weighted focus-measure. Information Fusion. 2015 Mar 1;22:105–18.
Cai J-H, Hu W-W. Feature Extraction of Gear Fault Signal Based on Sobel Operator and WHT. Shock and Vibration. 2013;20(3):551–9.
Chen W, Hu M, Zhou L, Gu H, Zhang X. Fusion Algorithm of Multi-focus Images with Weighted Ratios and Weighted Gradient Based on Wavelet Transform. Journal of Intelligent Systems. 2019 Oct 1;28(4):505–16.
A SAR image compression algorithm based on Mallat tower-type wavelet decomposition [Internet]. [cited 2021 Aug 31]. Available from: https://www.infona.pl/resource/bwmeta1.element.elsevier-9a5c21b6-84fc-3659-91ee-be9b2ff1b553
Yazid K, Zin MRM, Sayuti SB. Development of image fusion techniques :11.
Liu Y, Jin J, Wang Q, Shen Y, Dong X. Region level based multi-focus image fusion using quaternion wavelet and normalized cut. Signal Processing. 2014 Apr 1;97:9–30.
Multi-focus image fusion using HOSVD and edge intensity | Journal of Visual Communication and Image Representation [Internet]. [cited 2021 Aug 31]. Available from: https://dl.acm.org/doi/10.1016/j.jvcir.2017.02.006
Ismail MP bin, Sani S bin, Masenwat NA bin, Mohd S, Sayuti S, Ahmad MRB, et al. Radiation attenuation on labyrinth design bunker using Iridium-192 source. AIP Conference Proceedings. 2017 Jan 6;1799(1):050011.
Hemasree D, Reddy DSN, Rajeswari VR. Fusion of Panchromatic and Multispectral Image using PCA and Wavelet Transform. International Journal of Engineering Research & Technology
Mahyoub S, Fadil A, Mansour EM, Rhinane H, Al-Nahmi F. Fusing of Optical and Synthetic Aperture (SAR) Remote Sensing Data: A systematic literature review. Int Arch Photogramm Remote Sens Spatial Inf Sci. 2019 Feb 21;XLII-4/W12:127–38.
Fajaryati N, Budiyono, Akhyar M, Wiranto. The Employability Skills Needed To Face the Demands of Work in the Future: Systematic Literature Reviews. Open Engineering. 2020 Jan 1;10(1):595–603.
Patil DrS. An Efficient MRI Brain Image Registration and Wavelet Based Fusion. International Journal of Recent Technology and Engineering. 2019 Nov 1;8:10209–18.
Yadav N, Shrivastri S. A Review Of Image Fusion Using IHS With Wavelet Transform.
Ramesh C, Ranjith T. Fusion performance measures and a lifting wavelet transform based algorithm for image fusion. In: Proceedings of the Fifth International Conference on Information Fusion FUSION 2002 (IEEE CatNo02EX5997). 2002. p. 317–20 vol.1.
Abd-el-kader A, El-Din Moustafa H, Rehan S. Performance measures for image fusion based on wavelet transform and curvelet transform. In: 2011 28th National Radio Science Conference (NRSC). 2011. p. 1–7.
Morabito FC, Simone G, Cacciola M. 15 - Image fusion techniques for non-destructive testing and remote sensing applications. In: Stathaki T, editor. Image Fusion. Oxford: Academic Press; 2008. p. 367–92. Available from: https://www.sciencedirect.com/science/article/pii/B9780123725295000135
Mitianoudis N, Stathaki T. 12 - Enhancement of multiple sensor images using joint image fusion and blind restoration. In: Stathaki T, editor. Image Fusion [Internet]. Oxford: Academic Press; 2008. p. 299–326. Available from: https://www.sciencedirect.com/science/article/pii/B9780123725295000111
Silverman E. Convolutional Neural Networks for Cellular Automata Classification. In MIT Press; 2019 [cited 2021 Sep 1]. p. 280–1. Available from: https://direct.mit.edu/isal/article/doi/10.1162/isal_a_00175/99182/Convolutional-Neural-Networks-for-Cellular
Anoop Suraj A, Francis M, Kavya TS, Nirmal TM. Discrete wavelet transform based image fusion and de-noising in FPGA. Journal of Electrical Systems and Information Technology. 2014 May 1;1(1):72–81.