Saiprasad Ravishankar

696 total citations
40 papers, 383 citations indexed

About

Saiprasad Ravishankar is a scholar working on Radiology, Nuclear Medicine and Imaging, Computational Mechanics and Biomedical Engineering. According to data from OpenAlex, Saiprasad Ravishankar has authored 40 papers receiving a total of 383 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Radiology, Nuclear Medicine and Imaging, 20 papers in Computational Mechanics and 19 papers in Biomedical Engineering. Recurrent topics in Saiprasad Ravishankar's work include Sparse and Compressive Sensing Techniques (20 papers), Medical Imaging Techniques and Applications (19 papers) and Advanced MRI Techniques and Applications (13 papers). Saiprasad Ravishankar is often cited by papers focused on Sparse and Compressive Sensing Techniques (20 papers), Medical Imaging Techniques and Applications (19 papers) and Advanced MRI Techniques and Applications (13 papers). Saiprasad Ravishankar collaborates with scholars based in United States, Singapore and China. Saiprasad Ravishankar's co-authors include Jeffrey A. Fessler, Bihan Wen, Raj Rao Nadakuditi, Il Yong Chun, Brian E. Moore, Che‐Hung Liu, Minmin Zhou, Theodore B. Norris, Zhaohui Zhong and Yong Long and has published in prestigious journals such as Nature Photonics, IEEE Transactions on Image Processing and Optics Express.

In The Last Decade

Saiprasad Ravishankar

38 papers receiving 379 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Saiprasad Ravishankar United States 10 143 136 135 105 78 40 383
Lucio Azzari Finland 10 78 0.5× 68 0.5× 184 1.4× 34 0.3× 22 0.3× 13 328
Eunju Cha South Korea 7 104 0.7× 147 1.1× 104 0.8× 41 0.4× 15 0.2× 10 306
Hamed Mousavi Iran 3 144 1.0× 27 0.2× 28 0.2× 82 0.8× 24 0.3× 6 321
P. Andrés Spain 12 153 1.1× 14 0.1× 106 0.8× 52 0.5× 86 1.1× 23 531
Xiaohua Feng Singapore 12 334 2.3× 101 0.7× 33 0.2× 15 0.1× 57 0.7× 21 483
Dongeek Shin United States 8 295 2.1× 96 0.7× 139 1.0× 24 0.2× 78 1.0× 16 872
S. Vázquez-Montiel Mexico 8 140 1.0× 52 0.4× 56 0.4× 32 0.3× 66 0.8× 78 303
Jonas Adler Sweden 6 232 1.6× 184 1.4× 72 0.5× 23 0.2× 21 0.3× 14 367
Ronald A. Stack United States 13 245 1.7× 21 0.2× 109 0.8× 21 0.2× 121 1.6× 25 419

Countries citing papers authored by Saiprasad Ravishankar

Since Specialization
Citations

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

Fields of papers citing papers by Saiprasad Ravishankar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Saiprasad Ravishankar

This figure shows the co-authorship network connecting the top 25 collaborators of Saiprasad Ravishankar. A scholar is included among the top collaborators of Saiprasad Ravishankar 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 Saiprasad Ravishankar. Saiprasad Ravishankar 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.
Qu, Qing, et al.. (2025). Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction. IEEE Transactions on Computational Imaging. 11. 435–451. 2 indexed citations
2.
Klasky, Marc, et al.. (2025). Learning robust features for scatter removal and reconstruction in dynamic ICF X-ray tomography. Optics Express. 33(12). 26826–26826. 1 indexed citations
3.
Ravishankar, Saiprasad, et al.. (2024). Adaptive Local Neighborhood-Based Neural Networks for MR Image Reconstruction From Undersampled Data. IEEE Transactions on Computational Imaging. 10. 1235–1249. 1 indexed citations
4.
Wang, Rongrong, et al.. (2024). Diffusion-Based Adversarial Purification for Robust Deep Mri Reconstruction. 12841–12845. 3 indexed citations
5.
Wen, Bihan, Saiprasad Ravishankar, Zhizhen Zhao, Raja Giryes, & Jong Chul Ye. (2023). Physics-Driven Machine Learning for Computational Imaging: Part 2 [From the Guest Editors]. IEEE Signal Processing Magazine. 40(2). 13–15. 1 indexed citations
6.
Zha, Zhiyuan, Bihan Wen, Xin Yuan, et al.. (2023). Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing: Nonlocal sparse and low-rank modeling. IEEE Signal Processing Magazine. 40(1). 32–44. 38 indexed citations
7.
Qu, Qing, et al.. (2023). Robust Self-Guided Deep Image Prior. 1–5. 3 indexed citations
8.
Li, Hui, et al.. (2023). SMUG: Towards Robust Mri Reconstruction by Smoothed Unrolling. 1–5. 2 indexed citations
9.
Wang, Chong, et al.. (2022). REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration. 2022 IEEE International Conference on Image Processing (ICIP). 2886–2890. 3 indexed citations
10.
Ravishankar, Saiprasad, et al.. (2022). LONDN-MRI: Adaptive Local Neighborhood-Based Networks for MR Image Reconstruction from Undersampled Data. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). 97. 1–4. 1 indexed citations
11.
Guo, Lanqing, Zhiyuan Zha, Saiprasad Ravishankar, & Bihan Wen. (2022). Exploiting Non-Local Priors via Self-Convolution for Highly-Efficient Image Restoration. IEEE Transactions on Image Processing. 31. 1311–1324. 17 indexed citations
12.
Ye, Siqi, Zhipeng Li, Michael T. McCann, Yong Long, & Saiprasad Ravishankar. (2021). Unified Supervised-Unsupervised (SUPER) Learning for X-Ray CT Image Reconstruction. IEEE Transactions on Medical Imaging. 40(11). 2986–3001. 12 indexed citations
13.
Wen, Bihan, et al.. (2021). Labmat: Learned Feature-Domain Block Matching For Image Restoration. 1689–1693. 2 indexed citations
14.
Li, Zhipeng, Saiprasad Ravishankar, & Yong Long. (2019). Image-domain multi-material decomposition using a union of cross-material models. 63. 6–6. 3 indexed citations
15.
Ravishankar, Saiprasad, Anna Ma, & Deanna Needell. (2019). Analysis of fast structured dictionary learning [Image: see text]. PubMed Central. 3 indexed citations
16.
Li, Zhipeng, Saiprasad Ravishankar, Yong Long, & Jeffrey A. Fessler. (2019). DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering. IEEE Transactions on Medical Imaging. 39(4). 1223–1234. 23 indexed citations
17.
Ravishankar, Saiprasad, et al.. (2018). Deep dictionary-transform learning for image reconstruction. 1208–1212. 12 indexed citations
18.
Ravishankar, Saiprasad & Jeffrey A. Fessler. (2017). Data-driven Models and Approaches for Imaging. MW2C.4–MW2C.4. 1 indexed citations
19.
Ravishankar, Saiprasad, Brian E. Moore, Raj Rao Nadakuditi, & Jeffrey A. Fessler. (2017). Low-Rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging. IEEE Transactions on Medical Imaging. 36(5). 1116–1128. 47 indexed citations
20.
Ravishankar, Saiprasad, Raj Rao Nadakuditi, & Jeffrey A. Fessler. (2017). Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems. IEEE Transactions on Computational Imaging. 3(4). 694–709. 22 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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