Émilie Chouzenoux

3.2k total citations
112 papers, 1.5k citations indexed

About

Émilie Chouzenoux is a scholar working on Computational Mechanics, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Émilie Chouzenoux has authored 112 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Computational Mechanics, 30 papers in Computer Vision and Pattern Recognition and 24 papers in Artificial Intelligence. Recurrent topics in Émilie Chouzenoux's work include Sparse and Compressive Sensing Techniques (49 papers), Numerical methods in inverse problems (16 papers) and Image and Signal Denoising Methods (16 papers). Émilie Chouzenoux is often cited by papers focused on Sparse and Compressive Sensing Techniques (49 papers), Numerical methods in inverse problems (16 papers) and Image and Signal Denoising Methods (16 papers). Émilie Chouzenoux collaborates with scholars based in France, United Kingdom and India. Émilie Chouzenoux's co-authors include Jean‐Christophe Pesquet, Audrey Repetti, Angshul Majumdar, V́ıctor Elvira, Jérôme Idier, Saïd Moussaoui, Samy Ammari, Nathalie Lassau, Corinne Balleyguier and V. D. Elvira and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Power Systems and IEEE Transactions on Image Processing.

In The Last Decade

Émilie Chouzenoux

101 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Émilie Chouzenoux France 21 441 367 317 279 236 112 1.5k
Ming Yan United States 19 571 1.3× 301 0.8× 128 0.4× 220 0.8× 255 1.1× 86 1.3k
Rachel Ward United States 19 811 1.8× 339 0.9× 111 0.4× 366 1.3× 266 1.1× 64 1.4k
Gabriele Steidl Germany 21 707 1.6× 964 2.6× 191 0.6× 236 0.8× 185 0.8× 85 1.9k
Ben Adcock Canada 18 562 1.3× 323 0.9× 496 1.6× 125 0.4× 305 1.3× 72 1.4k
Luca Zanni Italy 18 615 1.4× 469 1.3× 174 0.5× 240 0.9× 220 0.9× 56 1.3k
Haomin Zhou United States 22 305 0.7× 495 1.3× 81 0.3× 144 0.5× 144 0.6× 90 1.9k
Kwangmoo Koh United States 7 1.0k 2.3× 730 2.0× 166 0.5× 401 1.4× 430 1.8× 9 2.5k
Bin Dong China 28 775 1.8× 958 2.6× 705 2.2× 291 1.0× 540 2.3× 121 2.9k
Peter Maaß Germany 32 584 1.3× 551 1.5× 332 1.0× 274 1.0× 578 2.4× 123 3.1k
Anders C. Hansen United Kingdom 17 405 0.9× 268 0.7× 481 1.5× 112 0.4× 266 1.1× 39 1.2k

Countries citing papers authored by Émilie Chouzenoux

Since Specialization
Citations

This map shows the geographic impact of Émilie Chouzenoux'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 Émilie Chouzenoux with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Émilie Chouzenoux more than expected).

Fields of papers citing papers by Émilie Chouzenoux

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Émilie Chouzenoux. 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 Émilie Chouzenoux. The network helps show where Émilie Chouzenoux may publish in the future.

Co-authorship network of co-authors of Émilie Chouzenoux

This figure shows the co-authorship network connecting the top 25 collaborators of Émilie Chouzenoux. A scholar is included among the top collaborators of Émilie Chouzenoux 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 Émilie Chouzenoux. Émilie Chouzenoux is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Chouzenoux, Émilie, et al.. (2024). A novel variational approach for multiphoton microscopy image restoration: from PSF estimation to 3D deconvolution. Inverse Problems. 40(6). 65003–65003.
2.
Goel, Anurag, et al.. (2024). DeConFCluster: Deep Convolutional Transform Learning based multiview clustering fusion framework. Signal Processing. 224. 109597–109597. 3 indexed citations
3.
Pesquet, Jean‐Christophe, et al.. (2024). Deep learning for automatic bowel-obstruction identification on abdominal CT. European Radiology. 34(9). 5842–5853. 4 indexed citations
4.
Chouzenoux, Émilie, et al.. (2023). An unrolled half-quadratic approach for sparse signal recovery in spectroscopy. Signal Processing. 218. 109369–109369. 3 indexed citations
5.
Chouzenoux, Émilie, et al.. (2023). Efficient bayes inference in neural networks through adaptive importance sampling. Journal of the Franklin Institute. 360(16). 12125–12149.
6.
Chouzenoux, Émilie, et al.. (2023). A computational two‐photon fluorescence approach for revealing label‐free the 3D image of viruses and bacteria. Journal of Biophotonics. 16(5). e202200266–e202200266. 1 indexed citations
7.
Chouzenoux, Émilie, et al.. (2023). Deep Unfolding of the DBFB Algorithm With Application to ROI CT Imaging With Limited Angular Density. IEEE Transactions on Computational Imaging. 9. 502–516. 12 indexed citations
8.
Chouzenoux, Émilie, et al.. (2023). PENDANTSS: PEnalized Norm-Ratios Disentangling Additive Noise, Trend and Sparse Spikes. IEEE Signal Processing Letters. 30. 215–219. 1 indexed citations
9.
Majumdar, Angshul, et al.. (2023). DeConDFFuse : Predicting drug–drug interaction using joint deep convolutional transform learning and decision forest fusion framework. Expert Systems with Applications. 227. 120238–120238. 5 indexed citations
10.
Chouzenoux, Émilie, et al.. (2023). Convergence Results for Primal-Dual Algorithms in the Presence of Adjoint Mismatch. SIAM Journal on Imaging Sciences. 16(1). 1–34. 4 indexed citations
11.
Mongia, Aanchal, et al.. (2022). DeepVir: Graphical Deep Matrix Factorization for In Silico Antiviral Repositioning—Application to COVID-19. Journal of Computational Biology. 29(5). 441–452. 3 indexed citations
12.
Chouzenoux, Émilie, et al.. (2022). Unmatched Preconditioning of the Proximal Gradient Algorithm. IEEE Signal Processing Letters. 29. 1122–1126. 7 indexed citations
13.
Chouzenoux, Émilie, et al.. (2021). Calibration-Less Multi-Coil Compressed Sensing Magnetic Resonance Image Reconstruction Based on OSCAR Regularization. Journal of Imaging. 7(3). 58–58. 4 indexed citations
14.
Lefort, Claire, et al.. (2021). FAMOUS: a fast instrumental and computational pipeline for multiphoton microscopy applied to 3D imaging of muscle ultrastructure. Journal of Physics D Applied Physics. 54(27). 274005–274005. 7 indexed citations
15.
16.
Ammari, Samy, Tarek Assi, Mehdi Touat, et al.. (2021). Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab. Diagnostics. 11(7). 1263–1263. 14 indexed citations
17.
Chouzenoux, Émilie, et al.. (2021). Convergence of proximal gradient algorithm in the presence of adjoint mismatch *. Inverse Problems. 37(6). 65009–65009. 10 indexed citations
18.
Chouzenoux, Émilie, et al.. (2019). Proximal approaches for matrix optimization problems: Application to robust precision matrix estimation. Signal Processing. 169. 107417–107417. 6 indexed citations
19.
Chouzenoux, Émilie, et al.. (2019). Deep unfolding of a proximal interior point method for image restoration. Inverse Problems. 36(3). 34005–34005. 72 indexed citations
20.
Pereyra, Marcelo, Philip Schniter, Émilie Chouzenoux, et al.. (2015). Tutorial on Stochastic Simulation and Optimization Methods in Signal Processing. arXiv (Cornell University). 5 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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