The DOI matches its Crossref record.
Multi-sensor image fusion using the wavelet transform
Abstract
In the image fusion scheme presented in this paper, the wavelet transforms of the input images are appropriately combined, and the new image is obtained by taking the inverse wavelet transform of the fused wavelet coefficients. An area-based maximum selection rule and a consistency verification step are used for feature selection. A performance measure using specially generated test images is also suggested.>
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Cited by
Multilevel features convolutional neural network for multifocus image fusion
Yang, Y., Nie, Z., Huang, S., Lin, P., & Wu, J. (2019) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2018.2889959 · Cited by 103
Multi-Focus Image Fusion: Algorithms, Evaluation, and a Library
Zafar, R., Farid, M. S., & Khan, M. H. (2020) · Journal of Imaging · doi:10.3390/jimaging6070060 · Cited by 29
Magnetic imaging and machine vision NDT for the on-line inspection of stainless steel strips
Ricci, M., Ficola, A., Fravolini, M. L., Battaglini, L., Palazzi, A., Burrascano, P., Valigi, P., Appolloni, L., Cervo, S., & Rocchi, C. (2013) · Measurement Science and Technology · doi:10.1088/0957-0233/24/2/025401 · Cited by 11
Metadata
- Authors
- Li, H., Manjunath, B. S., & Mitra, S. K.
- Venue
- Proceedings of 1st International Conference on Image Processing
- Publisher
- IEEE Comput. Soc. Press
- Published
- 2002-12-17
- Volume
- 1
- Pages
- 51-55
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2131168375
- Cited by
- 1,513
- References
- 0
- FWCI
- 112.56
- Type
- conference paper
- Language
- en
- Open access
- closed
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