The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Subpixel edge localization and the interpolation of still images
Abstract
In this paper, we present a nonlinear interpolation scheme for still image resolution enhancement. The algorithm is based on a source model emphasizing the visual integrity of detected edges and incorporates a novel edge fitting operator that has been developed for this application. A small neighborhood about each pixel in the low-resolution image is first mapped to a best-fit continuous space step edge. The bilevel approximation serves as a local template on which the higher resolution sampling grid can then be superimposed (where disputed values in regions of local window overlap are averaged to smooth errors). The result is an image of increased resolution with noticeably sharper edges and, in all tried cases, lower mean-squared reconstruction error than that produced by linear techniques.
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Citations
Cites
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
Cited by
An edge-guided image interpolation algorithm via directional filtering and data fusion
Zhang, L., & Wu, X. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.877407 · Cited by 1,020
Metadata
- Authors
- Jensen, K., & Anastassiou, D.
- Venue
- IEEE Transactions on Image Processing
- Published
- 1995-03
- Volume
- 4
- Issue
- 3
- Pages
- 285-295
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W1993392281
- Cited by
- 433
- References
- 1
- FWCI
- 5.77
- Type
- journal article
- Language
- en
- Open access
- closed
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- Jensen, K., & Anastassiou, D. (1995). Subpixel edge localization and the interpolation of still images. IEEE Transactions on Image Processing. https://doi.org/10.1109/83.366477
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.
