The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Fundamental limits of reconstruction-based superresolution algorithms under local translation
Abstract
Superresolution is a technique that can produce images of a higher resolution than that of the originally captured ones. Nevertheless, improvement in resolution using such a technique is very limited in practice. This makes it significant to study the problem: "Do fundamental limits exist for superresolution?" In this paper, we focus on a major class of superresolution algorithms, called the reconstruction-based algorithms, which compute high-resolution images by simulating the image formation process. Assuming local translation among low-resolution images, this paper is the first attempt to determine the explicit limits of reconstruction-based algorithms, under both real and synthetic conditions. Based on the perturbation theory of linear systems, we obtain the superresolution limits from the conditioning analysis of the coefficient matrix. Moreover, we determine the number of low-resolution images that are sufficient to achieve the limit. Both real and synthetic experiments are carried out to verify our analysis.
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Cites
Improving resolution by image registration
Irani, M., & Peleg, S. (1991) · CVGIP: Graphical Models and Image Processing · doi:10.1016/1049-9652(91)90045-l · Cited by 1,929
Limits on super-resolution and how to break them
Baker, S., & Kanade, T. (2002) · Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662) · doi:10.1109/cvpr.2000.854852 · Cited by 1,184
Elad, M., & Feuer, A. (1997) · IEEE Transactions on Image Processing · doi:10.1109/83.650118 · Cited by 1,035
Extraction of high-resolution frames from video sequences
Schultz, R. R., & Stevenson, R. L. (1996) · IEEE Transactions on Image Processing · doi:10.1109/83.503915 · Cited by 978
Multi-frame image restoration and registration
Huang, T. S. (1984) · Medical Entomology and Zoology · Cited by 976
Multiframe image restoration and registration
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Joint MAP registration and high-resolution image estimation using a sequence of undersampled images
Hardie, R. C., Barnard, K. J., & Armstrong, E. E. (1997) · IEEE Transactions on Image Processing · doi:10.1109/83.650116 · Cited by 826
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Freeman, W. T., & Pasztor, E. C. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790414 · Cited by 345
Borman, S., & Stevenson, R. L. (1998) · Cited by 209
Cited by
Super-resolution from a single image
Glasner, D., Bagon, S., & Irani, M. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459271 · Cited by 1,889
Image super-resolution using gradient profile prior
Sun, J., Xu, Z., & Shum, H.-Y. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587659 · Cited by 916
Self-citationDeep Network Cascade for Image Super-resolution
Cui, Z., Chang, H., Shan, S., Zhong, B., & Chen, X. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_4 · Cited by 255
Studying Very Low Resolution Recognition Using Deep Networks
Wang, Z., Chang, S., Yang, Y., Liu, D., & Huang, T. S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.518 · Cited by 235
Metadata
- Authors
- Lin, Z., & Shum, H.-Y.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2004-01
- Volume
- 26
- Issue
- 1
- Pages
- 83-97
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2163523041
- Cited by
- 426
- References
- 13
- FWCI
- 25.13
- Type
- journal article
- Language
- en
- Open access
- closed
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