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.
DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction
2017674 citationsGuang Yang, Simiao Yu et al.IEEE Transactions on Medical Imagingprofile →
Generative Joint Source-Channel Coding for Semantic Image Transmission
202384 citationsTze-Yang Tung, Pier Luigi Dragotti et al.IEEE Journal on Selected Areas in Communicationsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Pier Luigi Dragotti
Since
Specialization
Citations
This map shows the geographic impact of Pier Luigi Dragotti'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 Pier Luigi Dragotti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pier Luigi Dragotti more than expected).
Fields of papers citing papers by Pier Luigi Dragotti
This network shows the impact of papers produced by Pier Luigi Dragotti. 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 Pier Luigi Dragotti. The network helps show where Pier Luigi Dragotti may publish in the future.
Co-authorship network of co-authors of Pier Luigi Dragotti
This figure shows the co-authorship network connecting the top 25 collaborators of Pier Luigi Dragotti.
A scholar is included among the top collaborators of Pier Luigi Dragotti 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 Pier Luigi Dragotti. Pier Luigi Dragotti is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yang, Guang, Simiao Yu, Hao Dong, et al.. (2017). DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction. IEEE Transactions on Medical Imaging. 37(6). 1310–1321.674 indexed citations breakdown →
14.
Murray–Bruce, John & Pier Luigi Dragotti. (2014). Reconstructing diffusion fields sampled with a network of arbitrarily distributed sensors. Spiral (Imperial College London). 885–889.5 indexed citations
15.
Dragotti, Pier Luigi, et al.. (2014). Accurate image registration using approximate Strang-Fix and an application in super-resolution. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1063–1067.2 indexed citations
16.
Dragotti, Pier Luigi, et al.. (2012). A fast layer-based multiview image coding algorithm. TECNALIA Publications (Fundación TECNALIA Research & Innovation). 1224–1228.1 indexed citations
17.
Blu, Thierry, et al.. (2011). Sampling Curves with Finite Rate of Innovation. Infoscience (Ecole Polytechnique Fédérale de Lausanne).3 indexed citations
18.
Dragotti, Pier Luigi, et al.. (2011). Simultaneous estimation of sparse signals and systems at sub-Nyquist rates. European Signal Processing Conference. 328–332.1 indexed citations
19.
Velisavljević, Vladan, Baltasar Beferull‐Lozano, Martin Vetterli, & Pier Luigi Dragotti. (2003). Discrete Multi-Directional Wavelet Bases. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1. 1025–1028.14 indexed citations
20.
Dragotti, Pier Luigi, Martin Vetterli, & Vladan Velisavljević. (2002). Directional wavelets and wavelet footprints for compression and denoising. Infoscience (Ecole Polytechnique Fédérale de Lausanne).1 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.