W.C. Karl

5.9k total citations
206 papers, 3.9k citations indexed

About

W.C. Karl is a scholar working on Computer Vision and Pattern Recognition, Biomedical Engineering and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, W.C. Karl has authored 206 papers receiving a total of 3.9k indexed citations (citations by other indexed papers that have themselves been cited), including 74 papers in Computer Vision and Pattern Recognition, 68 papers in Biomedical Engineering and 65 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in W.C. Karl's work include Medical Imaging Techniques and Applications (52 papers), Medical Image Segmentation Techniques (43 papers) and Sparse and Compressive Sensing Techniques (27 papers). W.C. Karl is often cited by papers focused on Medical Imaging Techniques and Applications (52 papers), Medical Image Segmentation Techniques (43 papers) and Sparse and Compressive Sensing Techniques (27 papers). W.C. Karl collaborates with scholars based in United States, Türkiye and France. W.C. Karl's co-authors include Müjdat Çetin, Alan S. Willsky, Yonggang Shi, David A. Castañón, Neha Aggarwal, Ivana Stojanović, Mark R. Luettgen, George C. Verghese, Janusz Konrad and Paul Fieguth and has published in prestigious journals such as Nature, ACS Nano and PLoS ONE.

In The Last Decade

W.C. Karl

195 papers receiving 3.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
W.C. Karl United States 31 1.4k 1.2k 1.0k 833 599 206 3.9k
Yoram Bresler United States 37 2.1k 1.5× 1.5k 1.3× 577 0.6× 2.6k 3.2× 1.9k 3.2× 237 6.1k
Joseph A. O’Sullivan United States 32 946 0.7× 1.6k 1.4× 575 0.6× 281 0.3× 1.4k 2.3× 218 4.4k
Brendt Wohlberg United States 29 2.0k 1.4× 704 0.6× 232 0.2× 1.3k 1.5× 494 0.8× 137 3.7k
Gitta Kutyniok Germany 29 2.1k 1.5× 1.2k 1.1× 400 0.4× 2.6k 3.1× 551 0.9× 129 5.9k
K.S. Arun United States 15 1.8k 1.3× 535 0.5× 1.5k 1.4× 473 0.6× 318 0.5× 64 3.8k
Karl Kunisch Austria 49 1.3k 0.9× 843 0.7× 375 0.4× 4.9k 5.9× 549 0.9× 330 11.4k
Misha E. Kilmer United States 26 1.7k 1.2× 1.2k 1.0× 177 0.2× 1.9k 2.3× 1.3k 2.2× 65 5.0k
Thomas Blumensath United Kingdom 24 1.6k 1.1× 2.0k 1.7× 475 0.5× 3.9k 4.7× 689 1.2× 70 6.0k
Gongguo Tang United States 24 1.1k 0.8× 642 0.5× 601 0.6× 1.5k 1.8× 180 0.3× 88 3.9k
James H. McClellan United States 39 1.7k 1.2× 1.4k 1.2× 792 0.8× 2.0k 2.4× 139 0.2× 282 7.0k

Countries citing papers authored by W.C. Karl

Since Specialization
Citations

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

Fields of papers citing papers by W.C. Karl

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by W.C. Karl. 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 W.C. Karl. The network helps show where W.C. Karl may publish in the future.

Co-authorship network of co-authors of W.C. Karl

This figure shows the co-authorship network connecting the top 25 collaborators of W.C. Karl. A scholar is included among the top collaborators of W.C. Karl 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 W.C. Karl. W.C. Karl 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.
Karl, W.C., James E. Fowler, Charles A. Bouman, et al.. (2023). The Foundations of Computational Imaging: A signal processing perspective. IEEE Signal Processing Magazine. 40(5). 40–53. 5 indexed citations
2.
Karl, W.C., et al.. (2021). Autoregression and Structured Low-Rank Modeling of Sinogram Neighborhoods. IEEE Transactions on Computational Imaging. 7. 1044–1054. 3 indexed citations
3.
Karl, W.C., et al.. (2019). Fast Accurate CT Metal Artifact Reduction using Data Domain Deep Learning.. arXiv (Cornell University). 1 indexed citations
4.
Karl, W.C., et al.. (2015). Dictionary-based sparse representation for resolution improvement in laser voltage imaging of CMOS integrated circuits. Design, Automation, and Test in Europe. 597–600. 2 indexed citations
5.
Do, Synho, et al.. (2011). A decomposition-based CT reconstruction formulation for reducing blooming artifacts. Physics in Medicine and Biology. 56(22). 7109–7125. 19 indexed citations
6.
He, Lili, et al.. (2010). A Spatio-Temporal Deconvolution Method to Improve Perfusion CT Quantification. IEEE Transactions on Medical Imaging. 29(5). 1182–1191. 25 indexed citations
7.
Mendillo, M., et al.. (2007). The sources of sodium escaping from Io revealed by spectral high definition imaging. Nature. 448(7151). 330–332. 16 indexed citations
8.
Aggarwal, Neha & W.C. Karl. (2006). Line detection in images through regularized hough transform. IEEE Transactions on Image Processing. 15(3). 582–591. 178 indexed citations
9.
Ferencik, Maros, Jennifer B. Lisauskas, Ricardo C. Cury, et al.. (2006). Improved vessel morphology measurements in contrast-enhanced multi-detector computed tomography coronary angiography with non-linear post-processing. European Journal of Radiology. 57(3). 380–383. 3 indexed citations
10.
Karl, W.C., et al.. (2005). Coupled Shape Distribution-Based Segmentation of Multiple Objects. Lecture notes in computer science. 19. 345–356. 27 indexed citations
11.
Litvin, Andrey & W.C. Karl. (2003). Levelset based segmentation using data driven shape prior on feature histograms. 1 indexed citations
12.
Shi, Yonggang & W.C. Karl. (2003). A multiphase level set method for tomographic reconstruction of dynamic objects. 4 indexed citations
13.
Hoge, W. Scott, Eric L. Miller, H. Lev-Ari, et al.. (2001). An efficient region of interest acquisition method for dynamic magnetic resonance imaging. IEEE Transactions on Image Processing. 10(7). 1118–1128. 5 indexed citations
14.
Çetin, Müjdat & W.C. Karl. (2001). Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization. IEEE Transactions on Image Processing. 10(4). 623–631. 437 indexed citations
15.
Fieguth, Paul, et al.. (2000). Multiscale methods for the segmentation and reconstruction of signals and images. IEEE Transactions on Image Processing. 9(3). 456–468. 24 indexed citations
16.
Chan, Raymond C. K., W.C. Karl, & Robert S. Lees. (2000). A new model-based technique for enhanced small-vessel measurements in X-ray cine-angiograms. IEEE Transactions on Medical Imaging. 19(3). 243–255. 27 indexed citations
17.
Frakt, Austin B., W.C. Karl, & Alan S. Willsky. (1998). A multiscale hypothesis testing approach to anomaly detection and localization from noisy tomographic data. IEEE Transactions on Image Processing. 7(6). 825–837. 17 indexed citations
18.
Stadler, Robert, W.C. Karl, & Robert S. Lees. (1996). The application of echo-tracking methods to endothelium-dependent vasoreactivity and arterial compliance measurements. Ultrasound in Medicine & Biology. 22(1). 35–42. 22 indexed citations
19.
Luettgen, Mark R., W.C. Karl, & Alan S. Willsky. (1994). Efficient multiscale regularization with applications to the computation of optical flow. IEEE Transactions on Image Processing. 3(1). 41–64. 93 indexed citations
20.
Karl, W.C. & George C. Verghese. (1990). Curvatures of surfaces and their shadows. Linear Algebra and its Applications. 130. 231–255. 3 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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Rankless by CCL
2026