| Identifiability in blind deconvolution with subspace or sparsity constraints Y Li, K Lee, Y Bresler IEEE Transactions on information Theory 62 (7), 4266-4275, 2016 | 83 | 2016 |
| Blind recovery of sparse signals from subsampled convolution K Lee, Y Li, M Junge, Y Bresler IEEE Transactions on Information Theory 63 (2), 802-821, 2016 | 72 | 2016 |
| Identifiability in Bilinear Inverse Problems With Applications to Subspace or Sparsity-Constrained Blind Gain and Phase Calibration Y Li, K Lee, Y Bresler IEEE Transactions on Information Theory 63 (2), 822 - 842, 2016 | 67* | 2016 |
| When sparsity meets low-rankness: Transform learning with non-local low-rank constraint for image restoration B Wen, Y Li, Y Bresler 2017 IEEE international conference on acoustics, speech and signal …, 2017 | 57 | 2017 |
| Joint adaptive sparsity and low-rankness on the fly: an online tensor reconstruction scheme for video denoising B Wen, Y Li, L Pfister, Y Bresler Proceedings of the IEEE international conference on computer vision, 241-250, 2017 | 51 | 2017 |
| Global geometry of multichannel sparse blind deconvolution on the sphere Y Li, Y Bresler Advances in Neural Information Processing Systems 31, 2018 | 41 | 2018 |
| Image recovery via transform learning and low-rank modeling: The power of complementary regularizers B Wen, Y Li, Y Bresler IEEE Transactions on Image Processing 29, 5310-5323, 2020 | 36 | 2020 |
| Identifiability and Stability in Blind Deconvolution under Minimal Assumptions Y Li, K Lee, Y Bresler arXiv preprint arXiv:1507.01308, 2015 | 34 | 2015 |
| Optimal sample complexity for blind gain and phase calibration Y Li, K Lee, Y Bresler IEEE Transactions on Signal Processing 64 (21), 5549-5556, 2016 | 24 | 2016 |
| Blind gain and phase calibration via sparse spectral methods Y Li, K Lee, Y Bresler IEEE Transactions on Information Theory 65 (5), 3097-3123, 2018 | 21 | 2018 |
| Stability in blind deconvolution of sparse signals and reconstruction by alternating minimization K Lee, Y Li, M Junge, Y Bresler 2015 International Conference on Sampling Theory and Applications (SampTA …, 2015 | 21 | 2015 |
| Blind gain and phase calibration for low-dimensional or sparse signal sensing via power iteration Y Li, K Lee, Y Bresler 2017 International Conference on Sampling Theory and Applications (SampTA …, 2017 | 14 | 2017 |
| The power of complementary regularizers: Image recovery via transform learning and low-rank modeling B Wen, Y Li, Y Bresler arXiv preprint arXiv:1808.01316, 2018 | 13 | 2018 |
| Multichannel sparse blind deconvolution on the sphere Y Li, Y Bresler IEEE Transactions on Information Theory 65 (11), 7415-7436, 2019 | 12 | 2019 |
| Learning adaptive random features Y Li, K Zhang, J Wang, S Kumar Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 4229-4236, 2019 | 9 | 2019 |
| A set-theoretic study of the relationships of image models and priors for restoration problems B Wen, Y Li, Y Li, Y Bresler arXiv preprint arXiv:2003.12985, 2020 | 5 | 2020 |
| Unified theory for recovery of sparse signals in a general transform domain K Lee, Y Li, KH Jin, JC Ye IEEE Transactions on Information Theory 64 (8), 5457-5477, 2018 | 5 | 2018 |
| Bilinear inverse problems with sparsity: optimal identifiability conditions and efficient recovery Y Li | 4 | 2018 |
| Optimal sample complexity for stable matrix recovery Y Li, K Lee, Y Bresler 2016 IEEE International Symposium on Information Theory (ISIT), 81-85, 2016 | 3 | 2016 |
| Uniqueness in bilinear inverse problems with applications to subspace and joint sparsity models Y Li, K Lee, Y Bresler 2015 International Conference on Sampling Theory and Applications (SampTA …, 2015 | 3 | 2015 |
Yoram BreslerFounder Professor of Engineering, Depts. of Electrical & Computer Engineering and BioengineeringVerified email at illinois.edu
