Leah Bar

1.1k total citations
22 papers, 510 citations indexed

About

Leah Bar is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Leah Bar has authored 22 papers receiving a total of 510 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 8 papers in Computational Mechanics and 7 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Leah Bar's work include Advanced MRI Techniques and Applications (7 papers), Sparse and Compressive Sensing Techniques (6 papers) and Advanced Image Processing Techniques (6 papers). Leah Bar is often cited by papers focused on Advanced MRI Techniques and Applications (7 papers), Sparse and Compressive Sensing Techniques (6 papers) and Advanced Image Processing Techniques (6 papers). Leah Bar collaborates with scholars based in Israel, United States and Germany. Leah Bar's co-authors include Nir Sochen, Nahum Kiryati, Guillermo Sapiro, Martin Rumpf, Alexander Brook, Benjamin Berkels, Yoram Cohen, Benedikt Wirth, Darya Morozov and Shimon Abboud and has published in prestigious journals such as IEEE Transactions on Image Processing, Magnetic Resonance in Medicine and International Journal of Computer Vision.

In The Last Decade

Leah Bar

22 papers receiving 482 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Leah Bar Israel 11 312 145 134 98 42 22 510
Weihong Guo United States 10 313 1.0× 172 1.2× 201 1.5× 120 1.2× 101 2.4× 36 539
Martin Höller Austria 14 240 0.8× 189 1.3× 32 0.2× 231 2.4× 102 2.4× 41 613
Chengda Yang United States 3 413 1.3× 283 2.0× 159 1.2× 117 1.2× 96 2.3× 3 671
Hyuk Choi South Korea 8 373 1.2× 89 0.6× 125 0.9× 27 0.3× 38 0.9× 14 469
Stamatios Lefkimmiatis Switzerland 11 410 1.3× 260 1.8× 199 1.5× 133 1.4× 174 4.1× 27 693
Martin Welk Germany 11 319 1.0× 138 1.0× 67 0.5× 82 0.8× 42 1.0× 32 496
João Oliveira Portugal 8 468 1.5× 242 1.7× 191 1.4× 51 0.5× 111 2.6× 19 678
Caroline Chaux France 10 264 0.8× 124 0.9× 98 0.7× 36 0.4× 51 1.2× 27 390
Feng Huang China 11 408 1.3× 97 0.7× 49 0.4× 201 2.1× 66 1.6× 38 595
S. Ramani Switzerland 7 274 0.9× 348 2.4× 78 0.6× 438 4.5× 239 5.7× 10 785

Countries citing papers authored by Leah Bar

Since Specialization
Citations

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

Fields of papers citing papers by Leah Bar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Leah Bar

This figure shows the co-authorship network connecting the top 25 collaborators of Leah Bar. A scholar is included among the top collaborators of Leah Bar 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 Leah Bar. Leah Bar 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.
Bar, Leah, et al.. (2023). Deep Learning Solution of the Eigenvalue Problem for Differential Operators. Neural Computation. 35(6). 1100–1134. 8 indexed citations
2.
Bar, Leah & Nir Sochen. (2021). Strong Solutions for PDE-Based Tomography by Unsupervised Learning. SIAM Journal on Imaging Sciences. 14(1). 128–155. 22 indexed citations
3.
Morozov, Darya, Leah Bar, Nir Sochen, & Yoram Cohen. (2015). Pore sizes and directionality in microcapillaries from angular double-pulsed-field-gradient NMR. Microporous and Mesoporous Materials. 225. 105–115. 2 indexed citations
4.
Morozov, Darya, Leah Bar, Nir Sochen, & Yoram Cohen. (2014). Microstructural information from angular double‐pulsed‐field‐gradient NMR: From model systems to nerves. Magnetic Resonance in Medicine. 74(1). 25–32. 10 indexed citations
5.
Morozov, Darya, Leah Bar, Nir Sochen, & Yoram Cohen. (2013). Modeling of the diffusion MR signal in calibrated model systems and nerves. NMR in Biomedicine. 26(12). 1787–1795. 11 indexed citations
6.
Morozov, Darya, Leah Bar, Nir Sochen, & Yoram Cohen. (2012). Measuring small compartments with relatively weak gradients by angular double-pulsed-field-gradient NMR. Magnetic Resonance Imaging. 31(3). 401–407. 10 indexed citations
7.
Shemesh, Noam, Daniel Barazany, Ofer Sadan, et al.. (2012). Mapping apparent eccentricity and residual ensemble anisotropy in the gray matter using angular double‐pulsed‐field‐gradient MRI. Magnetic Resonance in Medicine. 68(3). 2 indexed citations
8.
Shemesh, Noam, Daniel Barazany, Ofer Sadan, et al.. (2011). Mapping apparent eccentricity and residual ensemble anisotropy in the gray matter using angular double‐pulsed‐field‐gradient MRI. Magnetic Resonance in Medicine. 68(3). 794–806. 35 indexed citations
9.
Bar, Leah & Guillermo Sapiro. (2011). Hierarchical invariant sparse modeling for image analysis. 25. 2397–2400. 3 indexed citations
10.
Bar, Leah & Guillermo Sapiro. (2010). Hierarchical dictionary learning for invariant classification. 3578–3581. 11 indexed citations
11.
Wirth, Benedikt, Leah Bar, Martin Rumpf, & Guillermo Sapiro. (2010). A Continuum Mechanical Approach to Geodesics in Shape Space. International Journal of Computer Vision. 93(3). 293–318. 36 indexed citations
12.
Bar, Leah & Guillermo Sapiro. (2009). Generalized Newton-Type Methods for Energy Formulations in Image Processing. SIAM Journal on Imaging Sciences. 2(2). 508–531. 18 indexed citations
13.
Bar, Leah & Guillermo Sapiro. (2008). Generalized Newton methods for energy formulations in image procesing. 809–812. 3 indexed citations
14.
Bar, Leah, Nir Sochen, & Nahum Kiryati. (2007). Convergence of an Iterative Method for Variational Deconvolution and Impulsive Noise Removal. Multiscale Modeling and Simulation. 6(3). 983–994. 3 indexed citations
15.
Bar, Leah, Alexander Brook, Nir Sochen, & Nahum Kiryati. (2007). Deblurring of Color Images Corrupted by Impulsive Noise. IEEE Transactions on Image Processing. 16(4). 1101–1111. 84 indexed citations
16.
Bar, Leah, Benjamin Berkels, Martin Rumpf, & Guillermo Sapiro. (2007). A Variational Framework for Simultaneous Motion Estimation and Restoration of Motion-Blurred Video. 1–8. 76 indexed citations
17.
Bar, Leah, Nir Sochen, & Nahum Kiryati. (2006). Semi-blind image restoration via Mumford-Shah regularization. IEEE Transactions on Image Processing. 15(2). 483–493. 57 indexed citations
18.
Bar, Leah, Nahum Kiryati, & Nir Sochen. (2006). Image Deblurring in the Presence of Impulsive Noise. International Journal of Computer Vision. 70(3). 279–298. 91 indexed citations
19.
Ring, Haim, Leah Bar, & Shimon Abboud. (1999). Functional correlates with left-right asymmetry of visual evoked potentials in stroke patients: Modeling and experimental results. Archives of Physical Medicine and Rehabilitation. 80(2). 166–172. 7 indexed citations
20.
Abboud, Shimon, et al.. (1995). Left-right asymmetry of visual evoked potentials in brain-damaged patients: A mathematical model and experimental results. Annals of Biomedical Engineering. 24(S1). 75–86. 14 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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