Vishal Monga

7.0k total citations
160 papers, 3.6k citations indexed

About

Vishal Monga is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Artificial Intelligence. According to data from OpenAlex, Vishal Monga has authored 160 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 84 papers in Computer Vision and Pattern Recognition, 48 papers in Aerospace Engineering and 32 papers in Artificial Intelligence. Recurrent topics in Vishal Monga's work include Advanced SAR Imaging Techniques (38 papers), Radar Systems and Signal Processing (28 papers) and Image Enhancement Techniques (26 papers). Vishal Monga is often cited by papers focused on Advanced SAR Imaging Techniques (38 papers), Radar Systems and Signal Processing (28 papers) and Image Enhancement Techniques (26 papers). Vishal Monga collaborates with scholars based in United States, France and Canada. Vishal Monga's co-authors include Muralidhar Rangaswamy, Brian L. Evans, Hojjat Seyed Mousavi, Umamahesh Srinivas, M.K. Mihcak, Khaled Alhujaili, Tiep H. Vu, Tiantong Guo, Omar Aldayel and Raghu G. Raj and has published in prestigious journals such as New England Journal of Medicine, IEEE Transactions on Pattern Analysis and Machine Intelligence and Applied Energy.

In The Last Decade

Vishal Monga

149 papers receiving 3.5k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Vishal Monga United States	31	1.9k	919	590	481	371	160	3.6k
Amir Averbuch Israel	31	1.6k 0.8×	591 0.6×	1.1k 1.9×	399 0.8×	496 1.3×	183	3.7k
Bo Chen China	31	1.6k 0.8×	1.7k 1.8×	1.5k 2.5×	409 0.9×	294 0.8×	211	4.4k
Jie Li China	33	1.9k 1.0×	733 0.8×	392 0.7×	498 1.0×	374 1.0×	177	3.5k
Patrick Bouthémy France	30	3.0k 1.6×	607 0.7×	448 0.8×	516 1.1×	327 0.9×	154	3.8k
Brian C. Lovell Australia	34	2.8k 1.4×	347 0.4×	1.1k 1.8×	550 1.1×	422 1.1×	263	4.3k
Panagiotis Tsakalides Greece	26	1.4k 0.7×	298 0.3×	389 0.7×	865 1.8×	880 2.4×	184	3.4k
Alin Achim United Kingdom	29	1.9k 1.0×	468 0.5×	357 0.6×	1.2k 2.5×	111 0.3×	156	3.3k
Andrew Calway United Kingdom	24	2.6k 1.4×	850 0.9×	499 0.8×	382 0.8×	235 0.6×	94	3.5k
Xiangyang Ji China	35	3.3k 1.7×	798 0.9×	1.1k 1.8×	630 1.3×	678 1.8×	252	5.3k
Tatsuya Harada Japan	34	2.1k 1.1×	475 0.5×	1.1k 1.9×	360 0.7×	253 0.7×	236	3.9k

Countries citing papers authored by Vishal Monga

Since Specialization

Citations

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

Fields of papers citing papers by Vishal Monga

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vishal Monga

This figure shows the co-authorship network connecting the top 25 collaborators of Vishal Monga. A scholar is included among the top collaborators of Vishal Monga 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 Vishal Monga. Vishal Monga 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

Mugamba, John, Peter Ssenyonga, Venkateswararao Cherukuri, et al.. (2025). Five-year outcomes after surgical treatment of infant postinfectious hydrocephalus in sub-Saharan Africa: results of a randomized trial. Journal of Neurosurgery Pediatrics. 36(2). 135–144.

Gini, Fulvio, et al.. (2025). Model-Based Learning for MIMO Radar Waveform Design in the Presence of Multiple Targets. IEEE Transactions on Aerospace and Electronic Systems. 61(3). 7165–7178. 2 indexed citations

Cook, Daniel, et al.. (2024). Deep Adaptive Phase Learning: Enhancing Synthetic Aperture Sonar Imagery Through Learned Coherent Autofocus. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 17. 9517–9532.

Zhao, Yanan, et al.. (2024). Deep, Convergent, Unrolled Half-Quadratic Splitting for Image Deconvolution. IEEE Transactions on Computational Imaging. 10. 574–588. 2 indexed citations

Brown, Daniel C., et al.. (2023). Resonant Scattering-Inspired Deep Networks for Munition Detection in 3D Sonar Imagery. IEEE Transactions on Geoscience and Remote Sensing. 61. 1–17. 7 indexed citations

Li, Yan, et al.. (2023). Small-signal stability enhancement of islanded microgrids via domain-enriched optimization. Applied Energy. 353. 122172–122172. 14 indexed citations

Cherukuri, Venkateswararao, Christine Hehnly, Brian Nsubuga Kaaya, et al.. (2023). Infection diagnosis in hydrocephalus CT images: a domain enriched attention learning approach. Journal of Neural Engineering. 20(3). 36033–36033. 1 indexed citations

Shi, Jun, Yanan Zhao, Wei Xiang, et al.. (2021). Deep Scattering Network With Fractional Wavelet Transform. IEEE Transactions on Signal Processing. 69. 4740–4757. 31 indexed citations

Cherukuri, Venkateswararao, Thomas O’Reilly, Mingzhao Yu, et al.. (2021). Assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus. NeuroImage Clinical. 32. 102896–102896. 7 indexed citations

10.

Guo, Tiantong, et al.. (2021). Physically Inspired Dense Fusion Networks for Relighting. 497–506. 3 indexed citations

11.

Li, Xuelu & Vishal Monga. (2020). Group based deep shared feature learning for fine-grained image classification. arXiv (Cornell University). 143. 9 indexed citations

12.

Alhujaili, Khaled, Xianxiang Yu, Guolong Cui, & Vishal Monga. (2020). Unimodular MIMO Radar Waveform Design Under Spectral Interference Constraints. 62. 157–162. 2 indexed citations

13.

Guo, Tiantong & Vishal Monga. (2020). Reinforced Depth-Aware Deep Learning for Single Image Dehazing. 8891–8895. 8 indexed citations

14.

Monga, Vishal, et al.. (2017). What's mine is yours: Pretrained CNNs for limited training sonar ATR. 1–7. 9 indexed citations

15.

Kulkarni, Abhaya V., Steven J. Schiff, John Mugamba, et al.. (2017). Endoscopic Treatment versus Shunting for Infant Hydrocephalus in Uganda. New England Journal of Medicine. 377(25). 2456–2464. 93 indexed citations

16.

Srinivas, Umamahesh, et al.. (2011). Image-adaptive Color Super-resolution. Color and Imaging Conference. 19(1). 120–125. 2 indexed citations

17.

Monga, Vishal, et al.. (2011). Design and Optimization of Color Lookup Tables on a Simplex Topology. IEEE Transactions on Image Processing. 21(4). 1981–1996. 8 indexed citations

18.

Bulan, Orhan, Gaurav Sharma, & Vishal Monga. (2010). Orientation Modulation for Data Hiding in Clustered-Dot Halftone Prints. IEEE Transactions on Image Processing. 19(8). 2070–2084. 36 indexed citations

19.

Monga, Vishal & Raja Bala. (2009). Shaped Local Regression and Its Application to Color Transforms. Color and Imaging Conference. 17(1). 272–277.

20.

Monga, Vishal & Raja Bala. (2007). Two-dimensional color correction: Recent advances. Color and Imaging Conference. 15(1). 48–53.

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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