The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Kernel Regression for Image Processing and Reconstruction
Abstract
In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more. Furthermore, we establish key relationships with some popular existing methods and show how several of these algorithms, including the recently popularized bilateral filter, are special cases of the proposed framework. The resulting algorithms and analyses are amply illustrated with practical examples.
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Citations
Cites
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 · cited 2×
“…in the last decade, several concepts related to the general theory we promote here have been rediscovered in different guises, and presented under different names such as normalized convolution [5], [6], bilateral filter [7], [8], edge-directed interpolation [9], and moving least squares [10].”
BackgroundNadaraya, E. A. (1964) · Theory of Probability & Its Applications · doi:10.1137/1109020 · Cited by 3,462
Cited by
The Little Engine That Could: Regularization by Denoising (RED)
Romano, Y., Elad, M., & Milanfar, P. (2017) · SIAM Journal on Imaging Sciences · doi:10.1137/16m1102884 · Cited by 934
Self-citationImage 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
Image super-resolution: the techniques, applications, and future
Yue, L., Shen, H., Li, J., Yuan, Q., Zhang, H., & Zhang, L. (2016) · Signal Processing · doi:10.1016/j.sigpro.2016.05.002 · Cited by 554
Yu, G., Sapiro, G., & Mallat, S. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2176743 · Cited by 561
Chiu, S. J., Allingham, M. J., Mettu, P. S., Cousins, S. W., Izatt, J. A., & Farsiu, S. (2015) · Biomedical Optics Express · doi:10.1364/boe.6.001172 · Cited by 377
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Metadata
- Authors
- Takeda, H., Farsiu, S., & Milanfar, P.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2007-02
- Volume
- 16
- Issue
- 2
- Pages
- 349-366
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2006262236
- Cited by
- 1,340
- References
- 2
- FWCI
- 60.66
- Type
- journal article
- Language
- en
- Open access
- closed
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- Takeda, H., Farsiu, S., & Milanfar, P. (2007). Kernel Regression for Image Processing and Reconstruction. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2006.888330
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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.
