Ron Rubinstein
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- Image and Signal Denoising Methods 9
- Advanced Data Compression Techniques 1
- Media Technology top 0.5%
- Advanced Image Fusion Techniques 2
- Computational Mechanics top 0.5%
- Sparse and Compressive Sensing Techniques 9
- Signal Processing top 1%
- Blind Source Separation Techniques 4
- Computational Mathematics top 5%
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- Photoacoustic and Ultrasonic Imaging 3
- Anatomy and Medical Technology 1
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- Ultrasonics and Acoustic Wave Propagation 2
Ron Rubinstein
11 papers receiving 2.6k citations
Hit Papers
Peers
Comparison fields: 5 of 110
- Computer Vision and Pattern Recognition 1.6k
- Media Technology 586
- Computational Mechanics 1.3k
- Signal Processing 544
- Computational Mathematics 22
Countries citing papers authored by Ron Rubinstein
This map shows the geographic impact of Ron Rubinstein'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 Ron Rubinstein with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ron Rubinstein more than expected).
Fields of papers citing papers by Ron Rubinstein
This network shows the impact of papers produced by Ron Rubinstein. 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 Ron Rubinstein. The network helps show where Ron Rubinstein may publish in the future.
Co-authorship network
The 10 scholars most cited alongside Ron Rubinstein, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 39 | |
| 2 | Adaptive image compression using sparse dictionaries | 2012 | 29 |
| 3 | Analysis K-SVD: A Dictionary-Learning Algorithm for the Analysis Sparse Modelbreakdown → | 2012 | 327 |
| 4 | Dictionaries for Sparse Representation Modeling Digital sampling can display signals, and it should be possible to expose a large part of the desired signal information with only a limited signal sample. | 2010 | 2 |
| 5 | Dictionaries for Sparse Representation Modelingbreakdown → | 2010 | 892 |
| 6 | Learning Sparse Dictionaries for Sparse Signal Approximation | 2009 | 10 |
| 7 | Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximationbreakdown → | 2009 | 428 |
| 8 | E-cient Implementation of the K-SVD Algorithm and the Batch-OMP Method | 2008 | 6 |
| 9 | Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuitbreakdown → | 2008 | 537 |
| 10 | Analysis versus synthesis in signal priorsbreakdown → | 2007 | 434 |
| 11 | Registration and display of multimodal images: applications in the extracranial head and neck region. | 1993 | 10 |
About Ron Rubinstein
Ron Rubinstein is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Signal Processing, having authored 11 papers that have together received 2.7k indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (9 papers), Sparse and Compressive Sensing Techniques (9 papers), Blind Source Separation Techniques (4 papers), Photoacoustic and Ultrasonic Imaging (3 papers), Advanced Image Fusion Techniques (2 papers), Ultrasonics and Acoustic Wave Propagation (2 papers), Anatomy and Medical Technology (1 paper) and Advanced Data Compression Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (1.6k citations), Media Technology (586 citations) and Computational Mechanics (1.3k citations). Ron Rubinstein has collaborated with scholars based in Israel and United States. Frequent co-authors include Michael Elad, Michael Zibulevsky, Alfred M. Bruckstein⋆, Peyman Milanfar, Tomer Peleg, Jean‐Yves Sichel, Roland Chisin, U. Pietrzyk, J M Gomori and Moshe Bocher. Their work appears in journals such as IEEE Transactions on Signal Processing, Proceedings of the IEEE, Inverse Problems, International Conference on Systems, Signals and Image Processing and PubMed.
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.