The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Super-Resolution With Sparse Mixing Estimators
Abstract
We introduce a class of inverse problem estimators computed by mixing adaptively a family of linear estimators corresponding to different priors. Sparse mixing weights are calculated over blocks of coefficients in a frame providing a sparse signal representation. They minimize an l1 norm taking into account the signal regularity in each block. Adaptive directional image interpolations are computed over a wavelet frame with an O(N logN) algorithm, providing state-of-the-art numerical results.
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Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Cited by
Yu, G., Sapiro, G., & Mallat, S. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2176743 · Cited by 561
Self-citationAccurate 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
Metadata
- Authors
- Mallat, S., & Yu, G.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2010-11
- Volume
- 19
- Issue
- 11
- Pages
- 2889-2900
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2103106600
- Cited by
- 296
- References
- 1
- FWCI
- 23.22
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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