Gabriel Eilertsen

1.4k total citations · 1 hit paper
27 papers, 842 citations indexed

About

Gabriel Eilertsen is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Gabriel Eilertsen has authored 27 papers receiving a total of 842 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computer Vision and Pattern Recognition, 7 papers in Artificial Intelligence and 5 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Gabriel Eilertsen's work include Image Enhancement Techniques (12 papers), Advanced Vision and Imaging (9 papers) and AI in cancer detection (6 papers). Gabriel Eilertsen is often cited by papers focused on Image Enhancement Techniques (12 papers), Advanced Vision and Imaging (9 papers) and AI in cancer detection (6 papers). Gabriel Eilertsen collaborates with scholars based in Sweden, United Kingdom and Italy. Gabriel Eilertsen's co-authors include Jonas Unger, Rafał Mantiuk, Joel Kronander, György Dénes, Claes Lundström, Per Larsson, Thomas Walter, Michael Teutsch, Francesco Banterle and Anders Ynnerman and has published in prestigious journals such as Scientific Reports, ACM Transactions on Graphics and Computers in Biology and Medicine.

In The Last Decade

Gabriel Eilertsen

26 papers receiving 811 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

HDR image reconstruction from a single exposure using deep CNNs

2017 360 citations Gabriel Eilertsen, Joel Kronander et al. ACM Transactions on Graphics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Gabriel Eilertsen Sweden	11	667	154	153	106	98	27	842
Manuela Pereira Portugal	17	542 0.8×	144 0.9×	177 1.2×	186 1.8×	57 0.6×	79	867
Andrea Polesel Italy	6	472 0.7×	40 0.3×	224 1.5×	113 1.1×	175 1.8×	8	724
Donggeun Yoo South Korea	8	592 0.9×	142 0.9×	136 0.9×	18 0.2×	42 0.4×	14	728
Christine Fernández-Maloigne France	13	366 0.5×	76 0.5×	158 1.0×	55 0.5×	79 0.8×	85	614
Lawrence A. Ray United States	9	257 0.4×	40 0.3×	59 0.4×	104 1.0×	86 0.9×	27	458
Said Pertuz Colombia	11	384 0.6×	99 0.6×	458 3.0×	196 1.8×	118 1.2×	47	889
Jean‐Charles Pinoli France	14	472 0.7×	22 0.1×	249 1.6×	60 0.6×	61 0.6×	55	665
Won-Dong Jang South Korea	13	852 1.3×	91 0.6×	305 2.0×	19 0.2×	26 0.3×	29	1.0k
Faliu Yi South Korea	17	347 0.5×	166 1.1×	229 1.5×	333 3.1×	117 1.2×	42	850

Countries citing papers authored by Gabriel Eilertsen

Since Specialization

Citations

This map shows the geographic impact of Gabriel Eilertsen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Gabriel Eilertsen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gabriel Eilertsen more than expected).

Fields of papers citing papers by Gabriel Eilertsen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gabriel Eilertsen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Gabriel Eilertsen. The network helps show where Gabriel Eilertsen may publish in the future.

Co-authorship network of co-authors of Gabriel Eilertsen

This figure shows the co-authorship network connecting the top 25 collaborators of Gabriel Eilertsen. A scholar is included among the top collaborators of Gabriel Eilertsen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Gabriel Eilertsen. Gabriel Eilertsen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Nilsson, Jens, Jonas Unger, & Gabriel Eilertsen. (2025). Self-Prioritizing Multi-Agent Reinforcement Learning for Conflict Resolution in Air Traffic Control with Limited Instructions. Aerospace. 12(2). 88–88.

Eilertsen, Gabriel, et al.. (2024). A Hybrid Sobolev Gradient Method for Learning NODEs. Operations Research Forum. 5(4). 1 indexed citations

Johnson, Ericka, et al.. (2024). Enhancing Tabular GAN Fairness: The Impact of Intersectional Feature Selection. 1146–1151. 1 indexed citations

Eilertsen, Gabriel, et al.. (2024). Out-of-distribution detection in digital pathology: Do foundation models bring the end to reconstruction-based approaches?. Computers in Biology and Medicine. 184. 109327–109327. 1 indexed citations

Eilertsen, Gabriel, et al.. (2023). Spatial uncertainty aggregation for false negatives detection in breast cancer metastases segmentation. 34–34. 1 indexed citations

Eilertsen, Gabriel, et al.. (2022). Generalisation effects of predictive uncertainty estimation in deep learning for digital pathology. Scientific Reports. 12(1). 8329–8329. 16 indexed citations

Unger, Jonas, et al.. (2022). Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications. 1(August 2022). 1–33. 18 indexed citations

Mantiuk, Rafał, et al.. (2022). Comparison of single image HDR reconstruction methods — the caveats of quality assessment. KTH Publication Database DiVA (KTH Royal Institute of Technology). 1–8. 26 indexed citations

Eilertsen, Gabriel, et al.. (2020). A Survey of Image Synthesis Methods for Visual Machine Learning. Computer Graphics Forum. 39(6). 426–451. 37 indexed citations

10.

Teutsch, Michael, et al.. (2020). An Evaluation of Objective Image Quality Assessment for Thermal Infrared Video Tone Mapping. 488–497. 4 indexed citations

11.

Eilertsen, Gabriel, et al.. (2020). Measuring Domain Shift for Deep Learning in Histopathology. IEEE Journal of Biomedical and Health Informatics. 25(2). 325–336. 148 indexed citations

12.

Eilertsen, Gabriel, Rafał Mantiuk, & Jonas Unger. (2019). Single-frame Regularization for Temporally Stable CNNs. Apollo (University of Cambridge). 12 indexed citations

13.

Eilertsen, Gabriel. (2018). The high dynamic range imaging pipeline : Tone-mapping, distribution, and single-exposure reconstruction. Linköping studies in science and technology. Dissertations. 2 indexed citations

14.

Eilertsen, Gabriel, Joel Kronander, György Dénes, Rafał Mantiuk, & Jonas Unger. (2017). HDR image reconstruction from a single exposure using deep CNNs. ACM Transactions on Graphics. 36(6). 1–15. 360 indexed citations breakdown →

15.

Unger, Jonas, Francesco Banterle, Rafał Mantiuk, & Gabriel Eilertsen. (2016). The HDR-video pipeline : From capture and image reconstruction to compression and tone mapping. KTH Publication Database DiVA (KTH Royal Institute of Technology). 1–6. 1 indexed citations

16.

Eilertsen, Gabriel, Rafał Mantiuk, & Jonas Unger. (2016). A high dynamic range video codec optimized by large-scale testing. 1379–1383. 8 indexed citations

17.

Unger, Jonas, Francesco Banterle, Gabriel Eilertsen, & Rafał Mantiuk. (2016). The HDR-video Pipeline. ISTI Open Portal. 6. 1 indexed citations

18.

Eilertsen, Gabriel, Rafał Mantiuk, & Jonas Unger. (2016). Real-time noise-aware tone-mapping and its use in luminance retargeting. 21. 894–898. 1 indexed citations

19.

Eilertsen, Gabriel, et al.. (2013). Evaluation of Tone Mapping Operators for HDR‐Video. Computer Graphics Forum. 32(7). 275–284. 53 indexed citations

20.

Eilertsen, Gabriel, et al.. (2013). Survey and evaluation of tone mapping operators for HDR video. KTH Publication Database DiVA (KTH Royal Institute of Technology). 1–1. 16 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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