Lean Fu

514 total citations · 1 hit paper
5 papers, 277 citations indexed

About

Lean Fu is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Infectious Diseases. According to data from OpenAlex, Lean Fu has authored 5 papers receiving a total of 277 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 3 papers in Media Technology and 0 papers in Infectious Diseases. Recurrent topics in Lean Fu's work include Advanced Image Processing Techniques (5 papers), Advanced Vision and Imaging (3 papers) and Image and Signal Denoising Methods (3 papers). Lean Fu is often cited by papers focused on Advanced Image Processing Techniques (5 papers), Advanced Vision and Imaging (3 papers) and Image and Signal Denoising Methods (3 papers). Lean Fu collaborates with scholars based in China. Lean Fu's co-authors include Ding Liu, Fangmin Chen, Fangyuan Kong, Mingxi Li, Songwei Liu, Jingwen He, Yang Bai, Jie Liu, Kai Chen and Shi Wu and has published in prestigious journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) and Proceedings of the AAAI Conference on Artificial Intelligence.

In The Last Decade

Lean Fu

5 papers receiving 268 citations

Hit Papers

align trajectories

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.

Residual Local Feature Network for Efficient Super-Resolution

2022 139 citations Fangyuan Kong, Mingxi Li et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Lean Fu China	5	255	131	12	11	7	5	277
Assaf Shocher Israel	4	246 1.0×	101 0.8×	6 0.5×	8 0.7×	5 0.7×	6	275
Jinglei Shi China	7	201 0.8×	60 0.5×	15 1.3×	14 1.3×	4 0.6×	15	235
Haoming Cai United States	3	149 0.6×	91 0.7×	7 0.6×	8 0.7×	3 0.4×	7	164
Meiguang Jin Switzerland	8	223 0.9×	85 0.6×	8 0.7×	7 0.6×	6 0.9×	14	242
Bahetiyaer Bare China	9	372 1.5×	194 1.5×	6 0.5×	12 1.1×	13 1.9×	16	386
Huixuan Tang Canada	7	334 1.3×	235 1.8×	23 1.9×	15 1.4×	7 1.0×	10	362
Liying Lu United States	4	214 0.8×	97 0.7×	8 0.7×	8 0.7×	15 2.1×	6	236
Miguel Granados Germany	5	225 0.9×	57 0.4×	13 1.1×	10 0.9×	8 1.1×	11	249
Zhetong Liang Hong Kong	6	268 1.1×	141 1.1×	9 0.8×	14 1.3×	2 0.3×	8	297
Yingqi Liu China	4	129 0.5×	89 0.7×	7 0.6×	7 0.6×	3 0.4×	7	148

Countries citing papers authored by Lean Fu

Since Specialization

Citations

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

Fields of papers citing papers by Lean Fu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lean Fu

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

All Works

Sort: Min cites: Since: Top N: Style:

5 of 5 papers shown

1.

Chen, Fangmin, et al.. (2023). Unfolding Once is Enough: A Deployment-Friendly Transformer Unit for Super-Resolution. 7952–7960. 4 indexed citations

2.

He, Jingwen, Shi Wu, Kai Chen, Lean Fu, & Chao Dong. (2022). GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 1879–1888. 48 indexed citations

3.

Kong, Fangyuan, Mingxi Li, Songwei Liu, et al.. (2022). Residual Local Feature Network for Efficient Super-Resolution. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 765–775. 139 indexed citations breakdown →

4.

Liu, Ding, et al.. (2022). Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 852–861. 62 indexed citations

5.

Dong, Hang, et al.. (2022). Deep Recurrent Neural Network with Multi-Scale Bi-directional Propagation for Video Deblurring. Proceedings of the AAAI Conference on Artificial Intelligence. 36(3). 3598–3607. 24 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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