| Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising K Zhang, W Zuo, Y Chen, D Meng, L Zhang IEEE transactions on image processing 26 (7), 3142-3155, 2017 | 2836 | 2017 |
| Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration Y Chen, T Pock IEEE transactions on pattern analysis and machine intelligence 39 (6), 1256-1272, 2016 | 670 | 2016 |
| Ntire 2017 challenge on single image super-resolution: Methods and results R Timofte, E Agustsson, L Van Gool, MH Yang, L Zhang Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 614 | 2017 |
| iPiano: Inertial proximal algorithm for nonconvex optimization P Ochs, Y Chen, T Brox, T Pock SIAM Journal on Imaging Sciences 7 (2), 1388-1419, 2014 | 310 | 2014 |
| On learning optimized reaction diffusion processes for effective image restoration Y Chen, W Yu, T Pock Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 274 | 2015 |
| LEARN: Learned experts’ assessment-based reconstruction network for sparse-data CT H Chen, Y Zhang, Y Chen, J Zhang, W Zhang, H Sun, Y Lv, P Liao, J Zhou, ... IEEE transactions on medical imaging 37 (6), 1333-1347, 2018 | 123 | 2018 |
| Insights into analysis operator learning: From patch-based sparse models to higher order MRFs Y Chen, R Ranftl, T Pock IEEE Transactions on Image Processing 23 (3), 1060-1072, 2014 | 106* | 2014 |
| Hyperspectral image restoration using low-rank tensor recovery H Fan, Y Chen, Y Guo, H Zhang, G Kuang IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2017 | 68 | 2017 |
| Revisiting loss-specific training of filter-based MRFs for image restoration Y Chen, T Pock, R Ranftl, H Bischof German Conference on Pattern Recognition, 271-281, 2013 | 64 | 2013 |
| Learning dynamic guidance for depth image enhancement S Gu, W Zuo, S Guo, Y Chen, C Chen, L Zhang Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 51 | 2017 |
| Learning -based analysis and synthesis sparsity priors using bi-level optimization Y Chen, T Pock, H Bischof arXiv preprint arXiv:1401.4105, 2014 | 33 | 2014 |
| A bi-level view of inpainting-based image compression Y Chen, R Ranftl, T Pock arXiv preprint arXiv:1401.4112, 2014 | 33 | 2014 |
| A higher-order MRF based variational model for multiplicative noise reduction Y Chen, W Feng, R Ranftl, H Qiao, T Pock IEEE signal processing letters 21 (11), 1370-1374, 2014 | 27 | 2014 |
| Learning non-local image diffusion for image denoising P Qiao, Y Dou, W Feng, R Li, Y Chen Proceedings of the 25th ACM international conference on Multimedia, 1847-1855, 2017 | 19 | 2017 |
| Fast and accurate Poisson denoising with trainable nonlinear diffusion W Feng, P Qiao, Y Chen IEEE transactions on cybernetics 48 (6), 1708-1719, 2017 | 17* | 2017 |
| 基于 SURF 的序列图像快速拼接方法 杨云涛， 冯莹， 曹毓， 陈运锦 计算机技术 21 (3), 2011 | 15 | 2011 |
| 光斑质心亚像素定位误差的实验研究 陈运锦， 冯莹， 魏立安， 赵辉， 朱志武 光電工程 37 (2), 80-84, 2010 | 14 | 2010 |
| Non-stationary speckle reduction in high resolution SAR images Z Xu, Q Shi, Y Chen, W Feng, Y Shao, L Sun, X Huang Digital Signal Processing 73, 72-82, 2018 | 11 | 2018 |
| Correlation Filter Tracking: Beyond an Open-loop System. Q Hu, Y Guo, Y Chen, J Xiao, W An BMVC, 2017 | 10 | 2017 |
| A new algorithm for optimizing TV-based PolSAR despeckling model X Nie, B Zhang, Y Chen, H Qiao IEEE Signal Processing Letters 23 (10), 1409-1413, 2016 | 6 | 2016 |
Thomas PockProfessor of Computer Science, TU GrazVerified email at icg.tugraz.at
