The DOI matches its Crossref record.
Comments on ‘Information measure for performance of image fusion’
Abstract
The unsuitability of using classic mutual information measure as a performance measure for image fusion is discussed. Analytical proof that classic mutual information cannot be considered a measure for image fusion performance is provided.
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Citations
Cited by
DM-Fusion: Deep Model-Driven Network for Heterogeneous Image Fusion
Xu, G., He, C., Wang, H., Zhu, H., & Ding, W. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3238511 · Cited by 57
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
Metadata
- Authors
- Hossny, M., Nahavandi, S., & Creighton, D.
- Venue
- Electronics Letters
- Published
- 2008-08-28
- Volume
- 44
- Issue
- 18
- Pages
- 1066-1067
- ISSN
- 0013-5194 (ISSN-L)
- 1350-911X
- OpenAlex
- W2034995331
- Cited by
- 505
- References
- 0
- FWCI
- 12.64
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Comments on ‘Information measure for performance of image fusion’. https://app.cochat.ai/papers/10.1049/el:20081754
- The paper
- Hossny, M., Nahavandi, S., & Creighton, D. (2008). Comments on ‘Information measure for performance of image fusion’. Electronics Letters. https://doi.org/10.1049/el%3A20081754
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
