David M. Strong

780 citations
16 papers · 524 indexed · h-index 6

Impact in

Papers in

David M. Strong

11 papers receiving 492 citations

Peers

David M. Strong
Comparison fields: 5 of 68
  • Computer Vision and Pattern Recognition 300
  • Media Technology 78
  • Computational Mechanics 162
  • Mathematical Physics 70
  • Biophysics 21
Replace Triet Le with:
Triet Le United States
Vincent Duval France
S. Alliney Italy
Rüyam Acar Türkiye
Seongjai Kim United States
Andreas Weinmann Germany
Frank Lenzen Germany
Greg Ongie United States
Chengda Yang United States
Ajil Jalal United States
David M. Strong relative to Triet Le United States Triet Le's profile →
Citations per field
00.5×1.5×
Triet Le · 1×
Citations per year

Countries citing papers authored by David M. Strong

Since Specialization
Citations

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

Fields of papers citing papers by David M. Strong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David M. Strong. 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 David M. Strong. The network helps show where David M. Strong may publish in the future.

Co-authorship network

The 10 scholars most cited alongside David M. Strong, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with David M. Strong Line = papers co-authored together David M. Strong links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 20240
2 20231
3 20230
4 20222
5 20221
6 20210
7 20156
8
Polarimeter Blind Deconvolution Using Image Diversity
20120
9 20070
10 200646
11 2003410
12
Load Balancing Search Algorithms on a Heterogeneous Cluster of PCs.
20011
13 20011
14 199746
15
Spatially adaptive local-feature-driven total variation minimizing image restoration
19975
16 19935

About David M. Strong

David M. Strong is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering, Astronomy and Astrophysics, Media Technology and Ocean Engineering, having authored 16 papers that have together received 524 indexed citations. Recurring topics across this work include Advanced Image Processing Techniques (5 papers), Image and Signal Denoising Methods (4 papers), Adaptive optics and wavefront sensing (3 papers), Spacecraft Design and Technology (3 papers), Space Satellite Systems and Control (3 papers), Medical Imaging Techniques and Applications (2 papers), Satellite Image Processing and Photogrammetry (2 papers) and Space exploration and regulation (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (300 citations), Media Technology (78 citations), Computational Mechanics (162 citations), Mathematical Physics (70 citations) and Biophysics (21 citations). David M. Strong has collaborated with scholars based in United States and France. Frequent co-authors include Tony F. Chan, Peter Blomgren, Jean–François Aujol, Francis K. Chun, Thomas G. Stockham, M. A. Matin, Gary B. Lamont, Hector Erives, Miguel Vélez-Reyes and Jesse B. Zydallis. Their work appears in journals such as The Journal of the Astronautical Sciences, Multiscale Modeling and Simulation, Inverse Problems, Optical Engineering and Advances in Space Research.

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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