Mingqin Chen

791 total citations · 1 hit paper
19 papers, 579 citations indexed

About

Mingqin Chen is a scholar working on Computer Vision and Pattern Recognition, Molecular Biology and Organic Chemistry. According to data from OpenAlex, Mingqin Chen has authored 19 papers receiving a total of 579 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computer Vision and Pattern Recognition, 6 papers in Molecular Biology and 4 papers in Organic Chemistry. Recurrent topics in Mingqin Chen's work include Advanced biosensing and bioanalysis techniques (6 papers), Image and Signal Denoising Methods (4 papers) and RNA Interference and Gene Delivery (3 papers). Mingqin Chen is often cited by papers focused on Advanced biosensing and bioanalysis techniques (6 papers), Image and Signal Denoising Methods (4 papers) and RNA Interference and Gene Delivery (3 papers). Mingqin Chen collaborates with scholars based in China, Singapore and Spain. Mingqin Chen's co-authors include Yuhui Quan, Hui Ji, Tongyao Pang, Feng Li, Xiuzhong Wang, Ting Hou, Shufeng Liu, Xiaojuan Liu, Limin Yang and Yi Qian and has published in prestigious journals such as Chemistry of Materials, IEEE Transactions on Signal Processing and Electrochimica Acta.

In The Last Decade

Mingqin Chen

17 papers receiving 565 citations

Hit Papers

Self2Self With Dropout: Learning Self-Supervised Denoisin... 2020 2026 2022 2024 2020 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mingqin Chen China 11 224 193 146 134 61 19 579
E. J. Wharton United States 15 466 2.1× 15 0.1× 213 1.5× 26 0.2× 34 0.6× 25 747
Jiangluqi Song China 17 105 0.5× 102 0.5× 234 1.6× 183 1.4× 577 9.5× 37 965
Junsheng Cao China 7 42 0.2× 72 0.4× 56 0.4× 77 0.6× 404 6.6× 16 581
Tae Keun Kim South Korea 10 331 1.5× 61 0.3× 169 1.2× 73 0.5× 84 1.4× 23 654
Xuelong Shi United States 14 25 0.1× 45 0.2× 62 0.4× 209 1.6× 35 0.6× 69 790
Zhiyuan Shen China 11 48 0.2× 89 0.5× 44 0.3× 145 1.1× 14 0.2× 29 457
Marcel Pfeifer Germany 9 44 0.2× 28 0.1× 35 0.2× 105 0.8× 45 0.7× 12 403
Katsuhiro Uno Japan 12 15 0.1× 80 0.4× 11 0.1× 29 0.2× 29 0.5× 40 396
Shengde Liu China 11 129 0.6× 80 0.4× 59 0.4× 126 0.9× 42 0.7× 46 370
Min Chang China 15 22 0.1× 36 0.2× 31 0.2× 165 1.2× 50 0.8× 50 517

Countries citing papers authored by Mingqin Chen

Since Specialization
Citations

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

Fields of papers citing papers by Mingqin Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mingqin Chen

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

All Works

19 of 19 papers shown
1.
Chen, Mingqin, Yuhui Quan, Yong Xu, & Hui Ji. (2022). Self-Supervised Blind Image Deconvolution via Deep Generative Ensemble Learning. IEEE Transactions on Circuits and Systems for Video Technology. 33(2). 634–647. 13 indexed citations
2.
Chen, Mingqin, Yuhui Quan, Tongyao Pang, & Hui Ji. (2022). Nonblind Image Deconvolution via Leveraging Model Uncertainty in An Untrained Deep Neural Network. International Journal of Computer Vision. 130(7). 1770–1789. 12 indexed citations
3.
Chen, Mingqin, et al.. (2022). Unsupervised Phase Retrieval Using Deep Approximate MMSE Estimation. IEEE Transactions on Signal Processing. 70. 2239–2252. 11 indexed citations
4.
Quan, Yuhui, et al.. (2022). High-Quality Self-Supervised Snapshot Hyperspectral Imaging. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 39. 1526–1530. 2 indexed citations
5.
6.
Quan, Yuhui, Mingqin Chen, Tongyao Pang, & Hui Ji. (2020). Self2Self With Dropout: Learning Self-Supervised Denoising From Single Image. 1887–1895. 250 indexed citations breakdown →
7.
Liang, Yun, et al.. (2020). Compression and denoising of time-resolved light transport. Optics Letters. 45(7). 1986–1986.
8.
Chen, Mingqin, et al.. (2019). Counting Attention Based on Classification Confidence for Visual Question Answering. 1173–1179. 2 indexed citations
9.
Liang, Yun, et al.. (2019). A data-driven compression method for transient rendering. 1–2. 1 indexed citations
10.
Liu, Xiaojuan, Mingqin Chen, Ting Hou, et al.. (2013). Label-free colorimetric assay for base excision repair enzyme activity based on nicking enzyme assisted signal amplification. Biosensors and Bioelectronics. 54. 598–602. 89 indexed citations
11.
Wang, Xiuzhong, Ting Hou, Wei Li, Mingqin Chen, & Feng Li. (2013). Highly sensitive and selective electrochemical identification of d-glucose based on specific concanavalin A combined with gold nanoparticles signal amplification. Sensors and Actuators B Chemical. 185. 105–109. 5 indexed citations
12.
Li, Feng, Mingqin Chen, Xinzhi Sun, Xiuzhong Wang, & Peng Li. (2013). A novel graphene oxide-based fluorescence assay for RNA endonuclease activity of mammalian Argonaute2 protein. Sensors and Actuators B Chemical. 182. 156–160. 9 indexed citations
13.
Liu, Xiaojuan, Mingqin Chen, Ting Hou, et al.. (2013). A novel electrochemical biosensor for label-free detection of uracil DNA glycosylase activity based on enzyme-catalyzed removal of uracil bases inducing strand release. Electrochimica Acta. 113. 514–518. 32 indexed citations
14.
Li, Feng, Limin Yang, Mingqin Chen, Yi Qian, & Bo Tang. (2012). A novel and versatile sensing platform based on HRP-mimicking DNAzyme-catalyzed template-guided deposition of polyaniline. Biosensors and Bioelectronics. 41. 903–906. 42 indexed citations
15.
Li, Feng, Limin Yang, Mingqin Chen, Peng Li, & Bo Tang. (2012). A selective amperometric sensing platform for lead based on target-induced strand release. The Analyst. 138(2). 461–466. 27 indexed citations
16.
17.
Hou, Hongwei, Xiangrong Ye, Xinquan Xin, et al.. (1995). Solid-State Synthesis, Crystal Structure, and Nonlinear Refractive and Absorptive Properties of the New Cluster (n-Bu4N)2[MoOS3Cu3BrCl2]. Chemistry of Materials. 7(3). 472–476. 41 indexed citations
18.
Huang, Zu‐En, et al.. (1987). Studies on rare earth allyl compounds. IV. The crystal structure of [Li2(μ-C3H5)(C4H8O2)3] [Ce(η3-C3H5)4]. Inorganica Chimica Acta. 139(1-2). 203–207. 19 indexed citations
19.
Green, Malcolm L. H., Gerard Parkin, Mingqin Chen, & Keith Prout. (1986). The chemistry of [W(PMe3)42-CH2PMe2)H]: synthesis of hydroxy-hydrido, fluoro-hydrido, and silyl-hydrido derivatives and the dimerisation of ethylene and propene giving η4-diene derivatives. Crystal structure of [W(PMe3)4H2(OH2)F]F. Journal of the Chemical Society Dalton Transactions. 2227–2236. 22 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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