Licheng Liu

1.9k total citations · 1 hit paper
63 papers, 1.4k citations indexed

About

Licheng Liu is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Artificial Intelligence. According to data from OpenAlex, Licheng Liu has authored 63 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Computer Vision and Pattern Recognition, 19 papers in Media Technology and 13 papers in Artificial Intelligence. Recurrent topics in Licheng Liu's work include Image and Signal Denoising Methods (21 papers), Advanced Image Processing Techniques (18 papers) and Sparse and Compressive Sensing Techniques (9 papers). Licheng Liu is often cited by papers focused on Image and Signal Denoising Methods (21 papers), Advanced Image Processing Techniques (18 papers) and Sparse and Compressive Sensing Techniques (9 papers). Licheng Liu collaborates with scholars based in China, Macao and United States. Licheng Liu's co-authors include C. L. Philip Chen, Long Chen, Yiqing Xu, Yuan Yan Tang, Ye Wang, Shutao Li, Yicong Zhou, Yushu Zhang, Xinge You and Jiantao Zhou and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Image Processing and IEEE Access.

In The Last Decade

Licheng Liu

61 papers receiving 1.3k citations

Hit Papers

A Practical Guide to Coun... 2022 2026 2023 2024 2022 50 100 150
What are hit papers?

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.

  1. A Practical Guide to Counterfactual Estimators for Causal Inference with Time‐Series Cross‐Sectional Data
    2022 163 citations Licheng Liu, Yiqing Xu et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Licheng Liu China 20 755 387 290 193 133 63 1.4k
Amit Saxena India 8 1.3k 1.7× 538 1.4× 456 1.6× 53 0.3× 98 0.7× 40 2.1k
Junmin Liu China 25 1.1k 1.5× 1.1k 2.8× 345 1.2× 119 0.6× 73 0.5× 102 2.0k
Karthikeyan Natesan Ramamurthy United States 17 469 0.6× 101 0.3× 667 2.3× 184 1.0× 46 0.3× 102 1.7k
Joseph Havlicek United States 19 553 0.7× 179 0.5× 107 0.4× 73 0.4× 57 0.4× 142 1.3k
Tong Sun United States 14 714 0.9× 357 0.9× 296 1.0× 58 0.3× 39 0.3× 56 1.2k
Xiaolin Li China 17 414 0.5× 118 0.3× 247 0.9× 125 0.6× 43 0.3× 41 1.1k
Lu Bai China 23 526 0.7× 130 0.3× 700 2.4× 61 0.3× 39 0.3× 81 1.4k
Yoonkyung Lee United States 14 423 0.6× 79 0.2× 618 2.1× 87 0.5× 65 0.5× 46 1.5k
Seba Susan India 19 447 0.6× 145 0.4× 707 2.4× 27 0.1× 79 0.6× 105 1.3k
Jianhua Z. Huang United States 15 234 0.3× 62 0.2× 230 0.8× 198 1.0× 101 0.8× 55 1.3k

Countries citing papers authored by Licheng Liu

Since Specialization
Citations

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

Fields of papers citing papers by Licheng Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Licheng Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Licheng Liu. A scholar is included among the top collaborators of Licheng Liu 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 Licheng Liu. Licheng Liu 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
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Xie, Ting, et al.. (2024). Multishot Compressive Hyperspectral Imaging Based on Tensor Fibered Rank Minimization and Its Primal-Dual Algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 17. 4466–4477. 2 indexed citations
3.
Liu, Licheng, et al.. (2024). MAPANet: A Multi-Scale Attention-Guided Progressive Aggregation Network for Multi-Contrast MRI Super-Resolution. IEEE Transactions on Computational Imaging. 10. 928–940. 4 indexed citations
4.
Yang, Bin, et al.. (2024). Change Representation and Extraction in Stripes: Rethinking Unsupervised Hyperspectral Image Change Detection With an Untrained Network. IEEE Transactions on Image Processing. 33. 5098–5113. 11 indexed citations
5.
Yang, Bin, et al.. (2024). Reciprocal Transformation-Based Joint Deep and Broad Learning for Change Detection With Heterogeneous Images. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–14. 2 indexed citations
6.
Liu, Licheng, et al.. (2023). Modal-Regression-Based Broad Learning System for Robust Regression and Classification. IEEE Transactions on Neural Networks and Learning Systems. 35(9). 12344–12357. 14 indexed citations
7.
Liu, Licheng, et al.. (2022). Self-Paced Broad Learning System. IEEE Transactions on Cybernetics. 53(6). 4029–4042. 32 indexed citations
8.
Li, Tianjun, et al.. (2022). Subspace-based minority oversampling for imbalance classification. Information Sciences. 621. 371–388. 24 indexed citations
9.
Wang, Caiyun, et al.. (2021). Identification and Validation of Four Novel Promoters for Gene Engineering with Broad Suitability across Species. Journal of Microbiology and Biotechnology. 31(8). 1154–1162. 1 indexed citations
10.
Liu, Licheng, C. L. Philip Chen, & Yaonan Wang. (2021). Modal Regression-Based Graph Representation for Noise Robust Face Hallucination. IEEE Transactions on Neural Networks and Learning Systems. 34(5). 2490–2502. 9 indexed citations
11.
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Liu, Licheng, et al.. (2021). A Bayesian Alternative to Synthetic Control for Comparative Case Studies. Political Analysis. 30(2). 269–288. 25 indexed citations
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Liu, Licheng, et al.. (2018). Fast Motion Estimation Algorithm for High Efficient Video Coding. 25. 6–10. 2 indexed citations
15.
Zhao, Zhangyan, et al.. (2018). A weighting intersection point prediction iteration optimization algorithm used in photogrammetry for port hoisting machinery. Optics & Laser Technology. 111. 323–330. 8 indexed citations
16.
Liu, Licheng, et al.. (2018). A Fast PM-Based DOA Estimation Method with Automatic Pairing. 1000. 386–390. 1 indexed citations
17.
Yang, Dazhi, Hao Quan, Vahid R. Disfani, & Licheng Liu. (2017). Reconciling solar forecasts: Geographical hierarchy. Solar Energy. 146. 276–286. 62 indexed citations
18.
Zhang, Yushu, Leo Yu Zhang, Jiantao Zhou, et al.. (2016). A Review of Compressive Sensing in Information Security Field. IEEE Access. 4. 2507–2519. 148 indexed citations
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20.
Zhou, Yicong, Weijia Cao, Licheng Liu, Sos С. Agaian, & C. L. Philip Chen. (2014). Fast Fourier transform using matrix decomposition. Information Sciences. 291. 172–183. 20 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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