| HDR image reconstruction from a single exposure using deep CNNs G Eilertsen, J Kronander, G Denes, RK Mantiuk, J Unger ACM transactions on graphics (TOG) 36 (6), 1-15, 2017 | 685 | 2017 |
| Measuring domain shift for deep learning in histopathology K Stacke, G Eilertsen, J Unger, C Lundström IEEE journal of biomedical and health informatics 25 (2), 325-336, 2020 | 321 | 2020 |
| Evaluation of tone mapping operators for HDR-video G Eilertsen, R Wanat, J Unger, RK Mantiuk Computer Graphics Forum (Proceedings of Pacific Graphics 2013) 32 (7), 275-284, 2013 | 162 | 2013 |
| A comparative review of tone-mapping algorithms for high dynamic range video G Eilertsen, RK Mantiuk, J Unger Computer Graphics Forum (Proceedings of Eurographics 2017) 36 (2), 2017 | 142 | 2017 |
| A closer look at domain shift for deep learning in histopathology K Stacke, G Eilertsen, J Unger, C Lundström arXiv preprint arXiv:1909.11575, 2019 | 104 | 2019 |
| Real-time noise-aware tone mapping G Eilertsen, RK Mantiuk, J Unger ACM Transactions on Graphics (TOG) 34 (6), 2015 | 99 | 2015 |
| A survey of image synthesis methods for visual machine learning A Tsirikoglou, G Eilertsen, J Unger Computer graphics forum 39 (6), 426-451, 2020 | 85 | 2020 |
| Survey of XAI in digital pathology M Pocevičiūtė, G Eilertsen, C Lundström Artificial intelligence and machine learning for digital pathology: state-of …, 2020 | 81 | 2020 |
| Classifying the classifier: dissecting the weight space of neural networks G Eilertsen, D Jönsson, T Ropinski, J Unger, A Ynnerman European Conference on Artificial Intelligence 325, 1119-1126, 2020 | 78 | 2020 |
| Single-frame regularization for temporally stable cnns G Eilertsen, RK Mantiuk, J Unger Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 58 | 2019 |
| Comparison of single image HDR reconstruction methods—the caveats of quality assessment P Hanji, R Mantiuk, G Eilertsen, S Hajisharif, J Unger ACM SIGGRAPH 2022 conference proceedings, 1-8, 2022 | 49 | 2022 |
| Generalisation effects of predictive uncertainty estimation in deep learning for digital pathology M Pocevičiūtė, G Eilertsen, S Jarkman, C Lundström Scientific Reports 12 (1), 8329, 2022 | 38 | 2022 |
| Learning representations with contrastive self-supervised learning for histopathology applications K Stacke, J Unger, C Lundström, G Eilertsen arXiv preprint arXiv:2112.05760, 2021 | 34 | 2021 |
| Unsupervised anomaly detection in digital pathology using GANs M Pocevičiūtė, G Eilertsen, C Lundström 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 1878-1882, 2021 | 30 | 2021 |
| Survey and evaluation of tone mapping operators for HDR video G Eilertsen, J Unger, R Wanat, R Mantiuk ACM SIGGRAPH 2013 Talks, 1-1, 2013 | 29 | 2013 |
| How to cheat with metrics in single-image HDR reconstruction G Eilertsen, S Hajisharif, P Hanji, A Tsirikoglou, RK Mantiuk, J Unger Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 28 | 2021 |
| Ensembles of GANs for synthetic training data generation G Eilertsen, A Tsirikoglou, C Lundström, J Unger arXiv preprint arXiv:2104.11797, 2021 | 24 | 2021 |
| The high dynamic range imaging pipeline: Tone-mapping, distribution, and single-exposure reconstruction G Eilertsen Linköping University Electronic Press, 2018 | 21 | 2018 |
| System and method for real-time tone-mapping J Unger, G Eilertsen, R Mantiuk US Patent 11,107,204, 2021 | 20 | 2021 |
| A high dynamic range video codec optimized by large-scale testing G Eilertsen, RK Mantiuk, J Unger IEEE International Conference on Image Processing (ICIP), 1379-1383, 2016 | 13 | 2016 |
Jonas UngerProfessor, division for Media and Information Technology, Linköping University, SwedenVerified email at liu.se
