The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Infrared and visible image fusion methods and applications: A survey
Abstract
Abstract Infrared images can distinguish targets from their backgrounds based on the radiation difference, which works well in all-weather and all-day/night conditions. By contrast, visible images can provide texture details with high spatial resolution and definition in a manner consistent with the human visual system. Therefore, it is desirable to fuse these two types of images, which can combine the advantages of thermal radiation information in infrared images and detailed texture information in visible images. In this work, we comprehensively survey the existing methods and applications for the fusion of infrared and visible images. First, infrared and visible image fusion methods are reviewed in detail. Meanwhile, image registration, as a prerequisite of image fusion, is briefly introduced. Second, we provide an overview of the main applications of infrared and visible image fusion. Third, the evaluation metrics of fusion performance are discussed and summarized. Fourth, we select eighteen representative methods and nine assessment metrics to conduct qualitative and quantitative experiments, which can provide an objective performance reference for different fusion methods and thus support relative engineering with credible and solid evidence. Finally, we conclude with the current status of infrared and visible image fusion and deliver insightful discussions and prospects for future work. This survey can serve as a reference for researchers in infrared and visible image fusion and related fields.
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Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
Bilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177
Shape matching and object recognition using shape contexts
Belongie, S., Malik, J., & Puzicha, J. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.993558 · Cited by 6,306
The Laplacian pyramid as a compact image code
Burt, P., & Adelson, E. (1983) · IEEE Transactions on Communications · doi:10.1109/tcom.1983.1095851 · Cited by 6,077
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
He, K., Sun, J., & Tang, X. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.213 · Cited by 5,546
Fast Discrete Curvelet Transforms
Candès, E., Demanet, L., Donoho, D., & Ying, L. (2006) · Multiscale Modeling & Simulation · doi:10.1137/05064182x · Cited by 2,573
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Anantrasirichai, N., & Bull, D. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10039-7 · Cited by 793
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Li, C., Song, D., Tong, R., & Tang, M. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2018.08.005 · Cited by 533
Self-citationDDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
Zhao, Z., Bai, H., Zhu, Y., Zhang, J., Xu, S., Zhang, Y., Zhang, K., Meng, D., Timofte, R., & Van Gool, L. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00742 · Cited by 431
Image Fusion Techniques: A Survey
Kaur, H., Koundal, D., & Kadyan, V. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09540-7 · Cited by 361
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Yue, J., Fang, L., Xia, S., Deng, Y., & Ma, J. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3322046 · Cited by 304
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Metadata
- Authors
- Ma, J., Ma, Y., & Li, C.
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 45
- Pages
- 153-178
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W2809795042
- Cited by
- 1,732
- References
- 12
- FWCI
- 92.17
- Type
- journal article
- Language
- en
- Open access
- closed
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