The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression
Open accessAbstract
Image super-resolution (SR) reconstruction is essentially an ill-posed problem, so it is important to design an effective prior. For this purpose, we propose a novel image SR method by learning both non-local and local regularization priors from a given low-resolution image. The non-local prior takes advantage of the redundancy of similar patches in natural images, while the local prior assumes that a target pixel can be estimated by a weighted average of its neighbors. Based on the above considerations, we utilize the non-local means filter to learn a non-local prior and the steering kernel regression to learn a local prior. By assembling the two complementary regularization terms, we propose a maximum a posteriori probability framework for SR recovery. Thorough experimental results suggest that the proposed SR method can reconstruct higher quality results both quantitatively and perceptually.
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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
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 1×
“Examples are edge-directed interpolation [3]–[5], normalized convolution [28], bilateral filter [29], [30], and steering kernel regression (SKR) [31].”
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Cited by
Second-Order Attention Network for Single Image Super-Resolution
Dai, T., Cai, J., Zhang, Y., Xia, S.-T., & Zhang, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01132 · Cited by 2,010
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks
Wang, Y., Wang, L., Wang, H., & Li, P. (2019) · IEEE Access · doi:10.1109/access.2019.2903582 · Cited by 143
Image super-resolution with densely connected convolutional networks
Kuang, P., Ma, T., Chen, Z., & Li, F. (2019) · Applied Intelligence · doi:10.1007/s10489-018-1234-y · Cited by 33
Ma, T., & Tian, W. (2021) · The Visual Computer · doi:10.1007/s00371-020-01843-3 · Cited by 16
Metadata
- Authors
- Zhang, K., Gao, X., Tao, D., & Li, X.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2012-11
- Volume
- 21
- Issue
- 11
- Pages
- 4544-4556
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W1992408872
- Cited by
- 576
- References
- 2
- FWCI
- 28.85
- Type
- journal article
- Language
- en
- Open access
- green
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- Zhang, K., Gao, X., Tao, D., & Li, X. (2012). Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2012.2208977
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