article · Journal of Electronic Imaging · 2014
Improvements of image fusion methods
Yotam Ben-Shoshan, Yitzhak Yitzhaky B
Cited by 6Abstract and details only
Abstract
Fusion of images from different imaging modalities, obtained by conventional fusion methods, may cause artifacts, including destructive superposition and brightness irregularities, in certain cases. This paper proposes two methods for improving image multimodal fusion quality. Based on the finding that a better fusion can be achieved when the images have a more positive correlation, the first method is a decision algorithm that runs at the preprocessing fusion stage and determines whether a complementary gray level of one of the input images should be used instead of the original one. The second method is suitable for multiresolution fusion, and it suggests choosing only one image from the lowest-frequency sub-bands in the pyramids, instead of combining values from both sub-bands. Experimental results indicate that the proposed fusion enhancement can reduce fusion artifacts. Quantitative fusion quality measures that support this conclusion are shown.
Cite this paper
Ben-Shoshan, Y., & B, Y. Y. (2014). Improvements of image fusion methods. Journal of Electronic Imaging, 23(02), 1–12. https://doi.org/10.1117/1.jei.23.2.023021
Ben-Shoshan, Yotam, and Yitzhak Yitzhaky B. “Improvements of image fusion methods.” Journal of Electronic Imaging, vol. 23, no. 02, 2014, pp. 1–12, https://doi.org/10.1117/1.jei.23.2.023021.
Ben-Shoshan, Yotam, and Yitzhak Yitzhaky B. 2014. “Improvements of image fusion methods.” Journal of Electronic Imaging 23 (02): 1–12. https://doi.org/10.1117/1.jei.23.2.023021.
Ben-Shoshan, Y. and B, Y.Y. (2014) ‘Improvements of image fusion methods’, Journal of Electronic Imaging, 23(02), pp. 1–12. Available at: https://doi.org/10.1117/1.jei.23.2.023021.
@article{benshoshan2014improvements,
title = {{Improvements of image fusion methods}},
author = {Ben-Shoshan, Yotam and B, Yitzhak Yitzhaky},
journal = {Journal of Electronic Imaging},
volume = {23},
number = {02},
pages = {1--12},
year = {2014},
doi = {10.1117/1.jei.23.2.023021}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.
Related papers
Image fusion for art analysis
Barbara Zitová, Miroslav Beneš, Jan Blažek · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
Cited by 9Open access
MULTIMODALITY AND MULTIRESOLUTION IMAGE FUSION
Paul M. de Zeeuw, Eric Pauwels, Jungong Han · International Conference on Computer Vision Theory and Applications · 2012
Cited by 6Open access
Image Fusion Techniques for Different Multimodality Medical Images Based on Various Conventional and Hybrid Algorithms for Disease Analysis
B. Rajalingam, R. Priya, R. Bhavani et al. · IGI Global eBooks · 2022
Cited by 2
Multimodal image seamless fusion
Kun Zhan, Lingwen Kong, Bo Liu et al. · Journal of Electronic Imaging · 2019
Cited by 25
Multimodality Medical Image Fusion Algorithm Based on Multi-iteration
Yin Dai, Huanzhen Wang, Kun Yao et al. · Advances in engineering research/Advances in Engineering Research · 2014
Cited by 0Open access
Enhancement of image fusion methods
Yotam Ben-Shoshan, Yitzhak Yitzhaky B · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
Cited by 4
Enhancement of Hybrid Multimodal Medical Image Fusion Techniques for Clinical Disease Analysis
B. Rajalingam, R. Priya · International Journal of Computer Vision and Image Processing · 2018
Cited by 10