The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution
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Cites
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
Accurate Blur Models vs. Image Priors in Single Image Super-resolution
Efrat, N., Glasner, D., Apartsin, A., Nadler, B., & Levin, A. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.352 · Cited by 200
Research works printed but not linked: 16
- Timofte, R., De Smet, V., Van Gool, L.: Anchored neighborhood regression for fast example-based super resolution. In: ICCV, pp. 1920–1927 (2013) · DOI · Paper page
- Sun, J., Xu, Z., Shum, H.-Y.: Image super-resolution using gradient profile prior. In: CVPR, pp. 1–8 (2008) · DOI · Paper page
- Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution as sparse representation of raw image patches. In: CVPR, pp. 1–8 (2008) · DOI · Paper page
- Sun, J., Zhu, J., Tappen, M.F.: Context-constrained hallucination for image super-resolution. In: CVPR, pp. 231–238 (2010) · DOI
- Michaeli, T., Irani, M.: Nonparametric blind super-resolution. In: ICCV, pp. 945–952 (2013) · DOI · Paper page
- Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning low-level vision. IJCV 40(1), 25–47 (2000) · DOI · Paper page
- Chang, H., Yeung, D.-Y., Xiong, Y.: Super-resolution through neighbor embedding. In: CVPR, pp. 275–282 (2004)
- Bevilacqua, M., Roumy, A., Guillemot, C., Alberi Morel, M.-L.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: BMVC, pp. 1–10 (2012) · DOI · Paper page
- Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D., Chenin, P., Cohen, A., Gout, C., Lyche, T., Mazure, M.-L., Schumaker, L. (eds.) Curves and Surfaces 2011. LNCS, vol. 6920, pp. 711–730. Springer, Heidelberg (2012) · DOI · Paper page
- Yang, C.-Y., Yang, M.-H.: Fast direct super-resolution by simple functions. In: ICCV, pp. 561–568 (2013) · DOI · Paper page
- Roweis, S., Lawrence, S.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000) · DOI · Paper page
- Timofte, R., Van Gool, L.: Adaptive and weighted collaborative representations for image classification. Pattern Recogn. Lett. 43, 127–135 (2014) · DOI
- Martin, D., Fowlkes, C., Tal, D., Malik, J.: A Database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: ICCV, pp. 416–423 (2001) · DOI · Paper page
- Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010) · DOI · Paper page
- De Smet, V., Namboodiri, V.P., Van Gool, L.J.: Nonuniform image patch exemplars for low level vision. In: WACV, pp. 23–30 (2013) · doi:10.1109/wacv.2013.6474995
- Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part IV. LNCS, vol. 8692, pp. 184–199. Springer, Heidelberg (2014) · DOI · Paper page
Cited by
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Johnson, J., Alahi, A., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_43 · Cited by 11,283
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2662206 · Cited by 8,983
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Kim, J., Lee, J. K., & Lee, K. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.182 · Cited by 7,870
“Sparse coding [25, 26, 21, 22] methods use a learned compact dictionary based on sparse signal representation.”
BackgroundMethodInfluential
Metadata
- Authors
- Timofte, R., De Smet, V., & Van Gool, L.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2015
- Pages
- 111-126
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W935139217
- Cited by
- 1,408
- References
- 2
- FWCI
- 71.63
- Type
- conference paper
- Language
- en
- Open access
- green
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
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- Timofte, R., De Smet, V., & Van Gool, L. (2015). A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-16817-3_8
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