Angshul Majumdar
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- Image and Signal Denoising Methods 52
- Face and Expression Recognition 24
- Media Technology top 0.5%
- Advanced Image Fusion Techniques 22
- Remote-Sensing Image Classification 19
- Computational Mathematics top 5%
- Signal Processing top 1%
- Blind Source Separation Techniques 40
- Computational Mechanics top 0.5%
- Sparse and Compressive Sensing Techniques 118
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- Advanced MRI Techniques and Applications 30
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- Photoacoustic and Ultrasonic Imaging 23
In The Last Decade
Angshul Majumdar
240 papers receiving 4.1k citations
Peers
Comparison fields: 5 of 141
- Computer Vision and Pattern Recognition 1.6k
- Media Technology 637
- Computational Mathematics 40
- Signal Processing 634
- Computational Mechanics 1.2k
Countries citing papers authored by Angshul Majumdar
This map shows the geographic impact of Angshul Majumdar'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 Angshul Majumdar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Angshul Majumdar more than expected).
Fields of papers citing papers by Angshul Majumdar
This network shows the impact of papers produced by Angshul Majumdar. 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 Angshul Majumdar. The network helps show where Angshul Majumdar may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Angshul Majumdar, 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 | 2024 | 0 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 3 | |
| 4 | 2024 | 2 | |
| 5 | 2024 | 0 | |
| 6 | 2023 | 0 | |
| 7 | 2023 | 5 | |
| 8 | 2022 | 70 | |
| 9 | 2022 | 3 | |
| 10 | 2022 | 5 | |
| 11 | 2020 | 1 | |
| 12 | 2019 | 38 | |
| 13 | 2018 | 30 | |
| 14 | 2018 | 40 | |
| 15 | Distributed elastic net regularized blind compressive sensing for recommender system design | 2014 | 2 |
| 16 | 2013 | 11 | |
| 17 | 2011 | 26 | |
| 18 | 2009 | 2 | |
| 19 | A Comparative Study in Wavelets, Curvelets and Contourlets as Feature Sets for Pattern Recognition | 2009 | 18 |
| 20 | Face Recognition by Multiresolution Contourlet Transform on Bit Quantized Facial Images. | 2007 | 2 |
About Angshul Majumdar
Angshul Majumdar is a scholar working on Computational Mechanics, Signal Processing, Computer Vision and Pattern Recognition, Media Technology and Acoustics and Ultrasonics, having authored 252 papers that have together received 4.2k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (118 papers), Image and Signal Denoising Methods (52 papers), Blind Source Separation Techniques (40 papers), Advanced MRI Techniques and Applications (30 papers), Face and Expression Recognition (24 papers), Photoacoustic and Ultrasonic Imaging (23 papers), Advanced Image Fusion Techniques (22 papers) and Remote-Sensing Image Classification (19 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.6k citations), Media Technology (637 citations), Computational Mathematics (40 citations), Signal Processing (634 citations) and Computational Mechanics (1.2k citations). Angshul Majumdar has collaborated with scholars based in India, Canada and France. Frequent co-authors include Rabab Ward, Hemant Kumar Aggarwal, Anupriya Gogna, Richa Singh, Mayank Vatsa, Aanchal Mongia, Vanika Singhal, Snigdha Tariyal, Debarka Sengupta and Émilie Chouzenoux. Their work appears in journals such as Magnetic Resonance Imaging, Signal Processing, IEEE Access, IEEE Transactions on Smart Grid and Biomedical Signal Processing and Control.
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.