The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
An efficient augmented Lagrangian method with applications to total variation minimization
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Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994
Donoho, D. L. (2004) · Cited by 16,703
Nonlinear total variation based noise removal algorithms
Rudin, L. I., Osher, S., & Fatemi, E. (1992) · Physica D: Nonlinear Phenomena · doi:10.1016/0167-2789(92)90242-f · Cited by 15,704
Candes, E. J., Romberg, J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.862083 · Cited by 15,639
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Candes, E. J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.885507 · Cited by 6,839
The Split Bregman Method for L1-Regularized Problems
Goldstein, T., & Osher, S. (2009) · SIAM Journal on Imaging Sciences · doi:10.1137/080725891 · Cited by 4,282
chambolle (2004) · doi:10.1023/b:jmiv.0000011321.19549.88 · Cited by 3,227
Two-Point Step Size Gradient Methods
BARZILAI, J., & BORWEIN, J. M. (1988) · IMA Journal of Numerical Analysis · doi:10.1093/imanum/8.1.141 · Cited by 2,873
Sparse Approximate Solutions to Linear Systems
Natarajan, B. K. (1995) · SIAM Journal on Computing · doi:10.1137/s0097539792240406 · Cited by 2,783
Cited by
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing
Zhang, J., & Ghanem, B. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00196 · Cited by 1,309
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements
Kulkarni, K., Lohit, S., Turaga, P., Kerviche, R., & Ashok, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.55 · Cited by 768
Metadata
- Authors
- Li, C., Yin, W., Jiang, H., & Zhang, Y.
- Venue
- Computational Optimization and Applications
- Published
- 2013-07-03
- Volume
- 56
- Issue
- 3
- Pages
- 507-530
- ISSN
- 0926-6003 (ISSN-L)
- 1573-2894
- OpenAlex
- W2130120519
- Cited by
- 656
- References
- 39
- FWCI
- 25.85
- Type
- journal article
- Language
- en
- Open access
- closed
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- Li, C., Yin, W., Jiang, H., & Zhang, Y. (2013). An efficient augmented Lagrangian method with applications to total variation minimization. Computational Optimization and Applications. https://doi.org/10.1007/s10589-013-9576-1
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.
