Gabriel Eilertsen
Cited by
Cited by
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
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
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
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
Real-time noise-aware tone mapping
G Eilertsen, RK Mantiuk, J Unger
ACM Transactions on Graphics (TOG) 34 (6), 2015
Survey of XAI in digital pathology
M Pocevičiūtė, G Eilertsen, C Lundström
Artificial intelligence and machine learning for digital pathology: state-of …, 2020
A survey of image synthesis methods for visual machine learning
A Tsirikoglou, G Eilertsen, J Unger
Computer Graphics Forum 39 (6), 426-451, 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
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
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
Learning representations with contrastive self-supervised learning for histopathology applications
K Stacke, J Unger, C Lundström, G Eilertsen
arXiv preprint arXiv:2112.05760, 2021
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
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
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
The high dynamic range imaging pipeline: Tone-mapping, distribution, and single-exposure reconstruction
G Eilertsen
Linköping University Electronic Press, 2018
Ensembles of GANs for synthetic training data generation
G Eilertsen, A Tsirikoglou, C Lundström, J Unger
arXiv preprint arXiv:2104.11797, 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
System and method for real-time tone-mapping
J Unger, G Eilertsen, R Mantiuk
US Patent 11,107,204, 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
Evaluation of Contrastive Predictive Coding for Histopathology Applications.
K Stacke, C Lundström, G Eilertsen
ML4H@ NeurIPS, 328-340, 2020
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