| From denoising to compressed sensing CA Metzler, A Maleki, RG Baraniuk IEEE Transactions on Information Theory 62 (9), 5117-5144, 2016 | 685 | 2016 |
| Deep learning techniques for inverse problems in imaging G Ongie, A Jalal, CA Metzler, RG Baraniuk, AG Dimakis, R Willett IEEE Journal on Selected Areas in Information Theory 1 (1), 39-56, 2020 | 561 | 2020 |
| Learned D-AMP: Principled Neural Network Based Compressive Image Recovery C Metzler, A Mousavi, R Baraniuk Advances in Neural Information Processing Systems, 1770-1781, 2017 | 289 | 2017 |
| prDeep: Robust Phase Retrieval with a Flexible Deep Network C Metzler, P Schniter, A Veeraraghavan, R Baraniuk International Conference on Machine Learning, 3498-3507, 2018 | 208 | 2018 |
| Deep optics for single-shot high-dynamic-range imaging CA Metzler, H Ikoma, Y Peng, G Wetzstein Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 172 | 2020 |
| Deep-inverse correlography: towards real-time high-resolution non-line-of-sight imaging CA Metzler, F Heide, P Rangarajan, MM Balaji, A Viswanath, ... Optica 7 (1), 63-71, 2020 | 135 | 2020 |
| Unsupervised learning with Stein's unbiased risk estimator CA Metzler, A Mousavi, R Heckel, RG Baraniuk arXiv preprint arXiv:1805.10531, 2018 | 94 | 2018 |
| BM3D-PRGAMP: Compressive phase retrieval based on BM3D denoising CA Metzler, A Maleki, RG Baraniuk 2016 IEEE International Conference on Image Processing (ICIP), 2504-2508, 2016 | 87 | 2016 |
| Coherent inverse scattering via transmission matrices: Efficient phase retrieval algorithms and a public dataset CA Metzler, MK Sharma, S Nagesh, RG Baraniuk, O Cossairt, ... 2017 IEEE International Conference on Computational Photography (ICCP), 1-16, 2017 | 78 | 2017 |
| Depth from defocus with learned optics for imaging and occlusion-aware depth estimation H Ikoma, CM Nguyen, CA Metzler, Y Peng, G Wetzstein 2021 IEEE International Conference on Computational Photography (ICCP), 1-12, 2021 | 67 | 2021 |
| BM3D-AMP: A new image recovery algorithm based on BM3D denoising CA Metzler, A Maleki, RG Baraniuk 2015 IEEE international conference on image processing (ICIP), 3116-3120, 2015 | 57 | 2015 |
| Keyhole imaging: non-line-of-sight imaging and tracking of moving objects along a single optical path CA Metzler, DB Lindell, G Wetzstein IEEE Transactions on Computational Imaging 7, 1-12, 2020 | 42* | 2020 |
| Imaging through extreme scattering in extended dynamic media AV Kanaev, AT Watnik, DF Gardner, C Metzler, KP Judd, P Lebow, ... Optics letters 43 (13), 3088-3091, 2018 | 33 | 2018 |
| Disambiguating monocular depth estimation with a single transient M Nishimura, DB Lindell, C Metzler, G Wetzstein European Conference on Computer Vision, 139-155, 2020 | 28 | 2020 |
| Inverse scattering via transmission matrices: Broadband illumination and fast phase retrieval algorithms MK Sharma, CA Metzler, S Nagesh, RG Baraniuk, O Cossairt, ... IEEE Transactions on Computational Imaging 6, 95-108, 2019 | 26 | 2019 |
| NeuWS: Neural wavefront shaping for guidestar-free imaging through static and dynamic scattering media BY Feng, H Guo, M Xie, V Boominathan, MK Sharma, A Veeraraghavan, ... Science Advances 9 (26), eadg4671, 2023 | 20 | 2023 |
| Roadmap on deep learning for microscopy G Volpe, C Wählby, L Tian, M Hecht, A Yakimovich, K Monakhova, ... ArXiv, 2023 | 14 | 2023 |
| Turbugan: An adversarial learning approach to spatially-varying multiframe blind deconvolution with applications to imaging through turbulence BY Feng, M Xie, CA Metzler IEEE Journal on Selected Areas in Information Theory 3 (3), 543-556, 2022 | 14 | 2022 |
| Deep S3PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models CA Metzler, G Wetzstein ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 14 | 2021 |
| D-VDAMP: Denoising-based approximate message passing for compressive MRI CA Metzler, G Wetzstein ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 11 | 2021 |
Richard BaraniukC. Sidney Burrus Professor, Electrical & Computer Engineering, Rice University在 rice.edu 的电子邮件经过验证
