Xiao-Guang Lv

64 total papers · 991 total citations
52 papers, 766 citations indexed

About

Xiao-Guang Lv is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Mathematical Physics. According to data from OpenAlex, Xiao-Guang Lv has authored 52 papers receiving a total of 766 indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Computer Vision and Pattern Recognition, 25 papers in Computational Mechanics and 12 papers in Mathematical Physics. Recurrent topics in Xiao-Guang Lv's work include Image and Signal Denoising Methods (34 papers), Sparse and Compressive Sensing Techniques (25 papers) and Advanced Image Processing Techniques (19 papers). Xiao-Guang Lv is often cited by papers focused on Image and Signal Denoising Methods (34 papers), Sparse and Compressive Sensing Techniques (25 papers) and Advanced Image Processing Techniques (19 papers). Xiao-Guang Lv collaborates with scholars based in China, United States and Hong Kong. Xiao-Guang Lv's co-authors include Ting‐Zhu Huang, Le Jiang, Jun Liu, Fang Li, Ivan Selesnick, Po‐Yu Chen, Zongben Xu, Jun Liu, Xi-Le Zhao and Si Wang and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Information Sciences.

In The Last Decade

Xiao-Guang Lv

51 papers receiving 737 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Xiao-Guang Lv 521 313 190 129 73 52 766
Gabriele Steidl 486 0.9× 200 0.6× 139 0.7× 63 0.5× 81 1.1× 36 813
Wayne Lawton 454 0.9× 162 0.5× 78 0.4× 69 0.5× 64 0.9× 45 791
Nelly Pustelnik 251 0.5× 239 0.8× 63 0.3× 113 0.9× 52 0.7× 59 647
Vincent Duval 370 0.7× 367 1.2× 155 0.8× 134 1.0× 48 0.7× 23 723
Tanja Teuber 492 0.9× 404 1.3× 146 0.8× 91 0.7× 43 0.6× 17 761
Ke Wei 297 0.6× 483 1.5× 27 0.1× 140 1.1× 30 0.4× 30 777
Triet Le 463 0.9× 175 0.6× 145 0.8× 71 0.6× 35 0.5× 17 635
Riccardo March 320 0.6× 199 0.6× 43 0.2× 34 0.3× 141 1.9× 44 815
Xiongjun Zhang 412 0.8× 520 1.7× 75 0.4× 30 0.2× 37 0.5× 50 792
S. Dimolitsas 371 0.7× 213 0.7× 43 0.2× 77 0.6× 75 1.0× 37 849

Countries citing papers authored by Xiao-Guang Lv

Since Specialization
Citations

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

Fields of papers citing papers by Xiao-Guang Lv

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xiao-Guang Lv. 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 Xiao-Guang Lv. The network helps show where Xiao-Guang Lv may publish in the future.

Co-authorship network of co-authors of Xiao-Guang Lv

This figure shows the co-authorship network connecting the top 25 collaborators of Xiao-Guang Lv. A scholar is included among the top collaborators of Xiao-Guang Lv 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 Xiao-Guang Lv. Xiao-Guang Lv is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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