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Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study
Abstract
Comparison of image processing techniques is critically important in deciding which algorithm, method, or metric to use for enhanced image assessment. Image fusion is a popular choice for various image enhancement applications such as overlay of two image products, refinement of image resolutions for alignment, and image combination for feature extraction and target recognition. Since image fusion is used in many geospatial and night vision applications, it is important to understand these techniques and provide a comparative study of the methods. In this paper, we conduct a comparative study on 12 selected image fusion metrics over six multiresolution image fusion algorithms for two different fusion schemes and input images with distortion. The analysis can be applied to different image combination algorithms, image processing methods, and over a different choice of metrics that are of use to an image processing expert. The paper relates the results to an image quality measurement based on power spectrum and correlation analysis and serves as a summary of many contemporary techniques for objective assessment of image fusion algorithms.
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Cites
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
The magical number seven, plus or minus two: Some limits on our capacity for processing information.
Miller, G. A. (1956) · Psychological Review · doi:10.1037/h0043158 · Cited by 17,812
Cited by
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
- Liu, Z., Blasch, E., Xue, Z., Zhao, J., Laganiere, R., & Wu, W.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2012-01
- Volume
- 34
- Issue
- 1
- Pages
- 94-109
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2142060261
- Cited by
- 717
- References
- 2
- FWCI
- 54.87
- Type
- journal article
- Language
- en
- Open access
- closed
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- Liu, Z., Blasch, E., Xue, Z., Zhao, J., Laganiere, R., & Wu, W. (2012). Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2011.109
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