Krishna Agarwal

1.6k total citations
85 papers, 1.0k citations indexed

About

Krishna Agarwal is a scholar working on Biomedical Engineering, Biophysics and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Krishna Agarwal has authored 85 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Biomedical Engineering, 28 papers in Biophysics and 25 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Krishna Agarwal's work include Advanced Fluorescence Microscopy Techniques (26 papers), Microwave Imaging and Scattering Analysis (18 papers) and Geophysical Methods and Applications (16 papers). Krishna Agarwal is often cited by papers focused on Advanced Fluorescence Microscopy Techniques (26 papers), Microwave Imaging and Scattering Analysis (18 papers) and Geophysical Methods and Applications (16 papers). Krishna Agarwal collaborates with scholars based in Norway, Singapore and India. Krishna Agarwal's co-authors include Xudong Chen, Yu Zhong, Rui Chen, Colin J. R. Sheppard, Radek Macháň, Dilip K. Prasad, Balpreet Singh Ahluwalia, Linfang Shen, Xiuzhu Ye and Åsa Birna Birgisdottir and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Krishna Agarwal

77 papers receiving 962 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Krishna Agarwal Norway 19 605 334 235 207 195 85 1.0k
George C. Giakos United States 15 488 0.8× 65 0.2× 55 0.2× 113 0.5× 282 1.4× 115 803
Reza Faraji‐Dana Iran 18 234 0.4× 198 0.6× 87 0.4× 58 0.3× 827 4.2× 149 1.1k
Hai Zhang China 16 344 0.6× 206 0.6× 17 0.1× 19 0.1× 218 1.1× 53 996
Elena Shchepakina Russia 14 367 0.6× 690 2.1× 131 0.6× 31 0.1× 510 2.6× 40 1.0k
Diego Caratelli Netherlands 24 359 0.6× 130 0.4× 50 0.2× 18 0.1× 1.2k 6.2× 186 1.9k
Chihiro Hayashi Japan 16 187 0.3× 67 0.2× 30 0.1× 54 0.3× 106 0.5× 78 1.1k
Mirco Raffetto Italy 15 274 0.5× 420 1.3× 148 0.6× 6 0.0× 504 2.6× 93 928
Alexandre Goy Switzerland 13 406 0.7× 472 1.4× 19 0.1× 115 0.6× 317 1.6× 34 1.1k
A. Cardama Spain 13 323 0.5× 155 0.5× 128 0.5× 13 0.1× 1.0k 5.1× 32 1.5k
Balaji Srinivasan India 20 192 0.3× 455 1.4× 26 0.1× 17 0.1× 819 4.2× 164 1.5k

Countries citing papers authored by Krishna Agarwal

Since Specialization
Citations

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

Fields of papers citing papers by Krishna Agarwal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Krishna Agarwal

This figure shows the co-authorship network connecting the top 25 collaborators of Krishna Agarwal. A scholar is included among the top collaborators of Krishna Agarwal 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 Krishna Agarwal. Krishna Agarwal 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.
Liu, Zicheng, et al.. (2025). Computational Comparison and Validation of Point Spread Functions for Optical Microscopes. IEEE Transactions on Computational Imaging. 11. 170–178.
2.
Gupta, Deepak K., et al.. (2024). An UltraMNIST classification benchmark to train CNNs for very large images. Scientific Data. 11(1). 771–771. 3 indexed citations
3.
Joshi, Himanshu, et al.. (2024). Compact Linnik‐type hyperspectral quantitative phase microscope for advanced classification of cellular components. Journal of Biophotonics. 17(8). e202400088–e202400088.
4.
Bhatt, Sunil, et al.. (2024). Characterizing the consistency of motion of spermatozoa through nanoscale motion tracing. PubMed. 5(3). 215–224. 3 indexed citations
5.
6.
Bhatt, Sunil, et al.. (2023). Single-shot multispectral quantitative phase imaging of biological samples using deep learning. Applied Optics. 62(15). 3989–3989. 12 indexed citations
7.
Ghosh, Biswajoy & Krishna Agarwal. (2023). Viewing life without labels under optical microscopes. Communications Biology. 6(1). 559–559. 21 indexed citations
8.
Agarwal, Krishna, et al.. (2023). Taxonomy of hybridly polarized Stokes vortex beams. Optics Express. 32(5). 7404–7404. 1 indexed citations
9.
Agarwal, Krishna, et al.. (2023). 3D full-wave multi-scattering forward solver for coherent microscopes. Optics Express. 31(9). 15015–15015. 2 indexed citations
10.
Liu, Zicheng, et al.. (2022). Physics-Guided Loss Functions Improve Deep Learning Performance in Inverse Scattering. IEEE Transactions on Computational Imaging. 8. 236–245. 20 indexed citations
11.
Agarwal, Krishna, et al.. (2021). Numerical method for tilt compensation in scanning acoustic microscopy. Measurement. 187. 110306–110306. 12 indexed citations
12.
Ahluwalia, Balpreet Singh, et al.. (2021). Mitochondrial dynamics and quantification of mitochondria‐derived vesicles in cardiomyoblasts using structured illumination microscopy. Journal of Biophotonics. 15(2). e202100305–e202100305. 8 indexed citations
13.
Ströhl, Florian, et al.. (2020). Object detection neural network improves Fourier ptychography reconstruction. Optics Express. 28(25). 37199–37199. 6 indexed citations
14.
Godtliebsen, Fred, et al.. (2020). Soft thresholding schemes for multiple signal classification algorithm. Optics Express. 28(23). 34434–34434. 7 indexed citations
15.
Ahluwalia, Balpreet Singh, et al.. (2020). Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning. Biomedical Optics Express. 12(1). 191–191. 3 indexed citations
16.
Agarwal, Krishna, Radek Macháň, & Dilip K. Prasad. (2018). Non-heuristic automatic techniques for overcoming low signal-to-noise-ratio bias of localization microscopy and multiple signal classification algorithm. Scientific Reports. 8(1). 4988–4988. 3 indexed citations
17.
Agarwal, Krishna & Dilip K. Prasad. (2017). Eigen-analysis reveals components supporting super-resolution imaging of blinking fluorophores. Scientific Reports. 7(1). 4445–4445. 4 indexed citations
18.
Wei, Zhun, Yue Ma, Yong‐Tao Cui, et al.. (2016). Quantitative analysis of effective height of probes in microwave impedance microscopy. Review of Scientific Instruments. 87(9). 94701–94701. 7 indexed citations
19.
Prasad, Dilip K. & Krishna Agarwal. (2016). Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes. Sensors. 16(3). 413–413. 3 indexed citations
20.
Agarwal, Krishna & Radek Macháň. (2016). Multiple signal classification algorithm for super-resolution fluorescence microscopy. Nature Communications. 7(1). 13752–13752. 68 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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