Vivek K Goyal

12.1k total citations · 1 hit paper
211 papers, 8.0k citations indexed

About

Vivek K Goyal is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Instrumentation. According to data from OpenAlex, Vivek K Goyal has authored 211 papers receiving a total of 8.0k indexed citations (citations by other indexed papers that have themselves been cited), including 75 papers in Computer Vision and Pattern Recognition, 53 papers in Computational Mechanics and 48 papers in Instrumentation. Recurrent topics in Vivek K Goyal's work include Advanced Optical Sensing Technologies (48 papers), Sparse and Compressive Sensing Techniques (47 papers) and Image and Signal Denoising Methods (43 papers). Vivek K Goyal is often cited by papers focused on Advanced Optical Sensing Technologies (48 papers), Sparse and Compressive Sensing Techniques (47 papers) and Image and Signal Denoising Methods (43 papers). Vivek K Goyal collaborates with scholars based in United States, Switzerland and United Kingdom. Vivek K Goyal's co-authors include Jelena Kovačević, Alexander A. Balandin, Sundeep Rangan, Alyson K. Fletcher, Martin Vetterli, Desalegne Teweldebrhan, Dongeek Shin, Jeffrey H. Shapiro, Ahmed Kirmani and Jonathan A. Kelner and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

Vivek K Goyal

202 papers receiving 7.5k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Multiple description coding: compression meets the network

2001 890 citations Vivek K Goyal profile →

Peers

Vivek K Goyal

CVPR

SP

INSTR

EEE

CM

David J. Brady United States

Marco F. Duarte United States

Yin Zhang China

Edmund Y. Lam Hong Kong

Gonzalo R. Arce United States

Mark A. Davenport United States

Zeev Zalevsky Israel

B. V. K. Vijaya Kumar United States

Karen Egiazarian Finland

Adolf W. Lohmann Germany

David J. Brady United States

Vivek K Goyal

3.2k ×1.2

CVPR

534 ×0.3

SP

629 ×0.4

INSTR

3.4k ×2.4

EEE

3.2k ×2.4

CM

Citations per year, relative to Vivek K Goyal Vivek K Goyal (= 1×) peers David J. Brady

Countries citing papers authored by Vivek K Goyal

Since Specialization

Citations

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

Fields of papers citing papers by Vivek K Goyal

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Vivek K Goyal. 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 Vivek K Goyal. The network helps show where Vivek K Goyal may publish in the future.

Co-authorship network of co-authors of Vivek K Goyal

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Li, Wenwen, Yijun Zhou, Wěi Li, et al.. (2025). Turning rough surfaces into non-line-of-sight cameras. Optica. 12(5). 626–626. 1 indexed citations

2.

Villa, Federica, et al.. (2023). Non-line-of-sight snapshots and background mapping with an active corner camera. Nature Communications. 14(1). 3677–3677. 10 indexed citations

3.

Saunders, Charles, et al.. (2022). Double Your Corners, Double Your Fun: The Doorway Camera. 1–12. 11 indexed citations

4.

Wu, Cheng, Xin Huang, Zheng-Ping Li, et al.. (2021). Non–line-of-sight imaging over 1.43 km. Proceedings of the National Academy of Sciences. 118(10). 108 indexed citations

5.

Li, Shuhui, Charles Saunders, Daniel J. Lum, et al.. (2021). Compressively sampling the optical transmission matrix of a multimode fibre. Light Science & Applications. 10(1). 88–88. 74 indexed citations

6.

Murray–Bruce, John, et al.. (2020). Two-Dimensional Non-Line-of-Sight Scene Estimation from a Single Edge\n Occluder. arXiv (Cornell University). 29 indexed citations

7.

Saunders, Charles, John Murray–Bruce, & Vivek K Goyal. (2019). Computational periscopy with an ordinary digital camera. Nature. 565(7740). 472–475. 140 indexed citations

8.

Altmann, Yoann, Stephen McLaughlin, Miles J. Padgett, et al.. (2018). Quantum-inspired computational imaging. Science. 361(6403). 136 indexed citations

9.

Murray–Bruce, John, et al.. (2018). Beyond Binomial and Negative Binomial: Adaptation in Bernoulli Parameter\n Estimation. arXiv (Cornell University). 3 indexed citations

10.

Shin, Dongeek, Feihu Xu, Dheera Venkatraman, et al.. (2016). Photon-efficient imaging with a single-photon camera. Nature Communications. 7(1). 12046–12046. 208 indexed citations

11.

Shin, Dongeek, Dheera Venkatraman, Rudi Lussana, et al.. (2016). Photon-efficient imaging with a single-photon camera. Nature. 2 indexed citations

12.

Kirmani, Ahmed, Dongeek Shin, Dheera Venkatraman, Franco N. C. Wong, & Vivek K Goyal. (2013). First-Photon Imaging: Scene Depth and Reflectance Acquisition from One Detected Photon per Pixel. 449–456. 3 indexed citations

13.

Weller, Daniel S., Jon̈athan R. Polimeni, Leo Grady, et al.. (2011). Denoising sparse images from GRAPPA using the nullspace method (DESIGN). DSpace@MIT (Massachusetts Institute of Technology). 2 indexed citations

14.

Weller, Daniel S., Jon̈athan R. Polimeni, Leo Grady, et al.. (2011). Denoising sparse images from GRAPPA using the nullspace method. Magnetic Resonance in Medicine. 68(4). 1176–1189. 19 indexed citations

15.

Adalsteinsson, Elfar, A. Zelinski, & Vivek K Goyal. (2010). Simultaneously Sparse Solutions to Linear Inverse Problems with Multiple System Matrices and a Single Observation Vector. SIAM Journal on Control and Optimization. 2 indexed citations

16.

Rangan, Sundeep, Vivek K Goyal, & Alyson K. Fletcher. (2009). Asymptotic Analysis of MAP Estimation via the Replica Method and Compressed Sensing. Neural Information Processing Systems. 22. 1545–1553. 22 indexed citations

17.

Jafarpour, Behnam, Vivek K Goyal, Dennis McLaughlin, & William T. Freeman. (2009). Transform-domain sparsity regularization for inverse problems in geosciences. Geophysics. 74(5). R69–R83. 62 indexed citations

18.

Rangan, Sundeep, Vivek K Goyal, & Alyson K. Fletcher. (2008). Resolution Limits of Sparse Coding in High Dimensions. Neural Information Processing Systems. 21. 449–456. 3 indexed citations

19.

Zelinski, A., Lawrence L. Wald, Kawin Setsompop, et al.. (2008). Fast slice‐selective radio‐frequency excitation pulses for mitigating B inhomogeneity in the human brain at 7 Tesla. Magnetic Resonance in Medicine. 59(6). 1355–1364. 59 indexed citations

20.

Goyal, Vivek K, Jelena Kovačević, & Jonathan A. Kelner. (2001). Quantized Frame Expansions with Erasures. Applied and Computational Harmonic Analysis. 10(3). 203–233. 309 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.

Explore authors with similar magnitude of impact