Dai-Qiang Chen

445 total citations
25 papers, 340 citations indexed

About

Dai-Qiang Chen is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Media Technology. According to data from OpenAlex, Dai-Qiang Chen has authored 25 papers receiving a total of 340 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computer Vision and Pattern Recognition, 15 papers in Computational Mechanics and 4 papers in Media Technology. Recurrent topics in Dai-Qiang Chen's work include Image and Signal Denoising Methods (17 papers), Sparse and Compressive Sensing Techniques (14 papers) and Advanced Image Processing Techniques (8 papers). Dai-Qiang Chen is often cited by papers focused on Image and Signal Denoising Methods (17 papers), Sparse and Compressive Sensing Techniques (14 papers) and Advanced Image Processing Techniques (8 papers). Dai-Qiang Chen collaborates with scholars based in China, Argentina and Taiwan. Dai-Qiang Chen's co-authors include Lizhi Cheng, Yan Zhou, Hui Zhang, Ying Li, Wei Chen, Liwen Tan, Weiren Shi, Fang Su, Jie‐Zhi Cheng and Xiaoguang Tu and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Image Processing and Applied Microbiology and Biotechnology.

In The Last Decade

Dai-Qiang Chen

24 papers receiving 328 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dai-Qiang Chen China 11 252 154 70 56 43 25 340
Zhi‐Feng Pang China 11 285 1.1× 104 0.7× 91 1.3× 53 0.9× 30 0.7× 53 385
Laurent Demaret Germany 11 209 0.8× 102 0.7× 44 0.6× 23 0.4× 44 1.0× 20 343
Hongbin Guo United States 8 140 0.6× 49 0.3× 23 0.3× 124 2.2× 27 0.6× 22 325
Stéphanie Jehan‐Besson France 13 488 1.9× 61 0.4× 54 0.8× 71 1.3× 27 0.6× 23 555
Ginmo Chung United States 4 175 0.7× 76 0.5× 34 0.5× 17 0.3× 18 0.4× 4 217
J. Shah United States 9 229 0.9× 68 0.4× 34 0.5× 34 0.6× 15 0.3× 18 295
François Malgouyres France 11 406 1.6× 171 1.1× 117 1.7× 23 0.4× 36 0.8× 34 472
Miyoun Jung South Korea 10 294 1.2× 158 1.0× 99 1.4× 18 0.3× 24 0.6× 29 365
J.-C. Pesquet France 10 224 0.9× 65 0.4× 69 1.0× 9 0.2× 15 0.3× 24 292
Pablo Arias France 11 427 1.7× 73 0.5× 156 2.2× 16 0.3× 33 0.8× 34 507

Countries citing papers authored by Dai-Qiang Chen

Since Specialization
Citations

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

Fields of papers citing papers by Dai-Qiang Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dai-Qiang Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Dai-Qiang Chen. A scholar is included among the top collaborators of Dai-Qiang 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 Dai-Qiang Chen. Dai-Qiang Chen 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
1.
Chen, Dai-Qiang. (2025). Hybrid sparse and dense attentions of similar regions for image denoising. Signal Image and Video Processing. 19(6).
2.
Chen, Dai-Qiang, et al.. (2024). Nitrogen Removal Performance and Microbial Community Structure of IMTA Ponds (Apostistius japonicus-Penaeus japonicus-Ulva). Microbial Ecology. 87(1). 82–82. 2 indexed citations
3.
Li, Meng, Dai-Qiang Chen, Jitao Li, et al.. (2024). Profile of the gut microbiota of Pacific white shrimp under industrial indoor farming system. Applied Microbiology and Biotechnology. 108(1). 225–225. 5 indexed citations
4.
Chen, Dai-Qiang. (2023). Down-scale simplified non-local attention networks with application to image denoising. Signal Image and Video Processing. 18(1). 47–54. 1 indexed citations
5.
Tu, Xiaoguang, Mei Xie, Jingjing Gao, et al.. (2017). Automatic Categorization and Scoring of Solid, Part-Solid and Non-Solid Pulmonary Nodules in CT Images with Convolutional Neural Network. Scientific Reports. 7(1). 8533–8533. 30 indexed citations
6.
Chen, Dai-Qiang. (2016). Data-Driven Tight Frame Learning Scheme Based on Local and Non-local Sparsity with Application to Image Recovery. Journal of Scientific Computing. 69(2). 461–486. 3 indexed citations
7.
Chen, Dai-Qiang, Yan Zhou, & Lijuan Song. (2016). Fixed point algorithm based on adapted metric method for convex minimization problem with application to image deblurring. Advances in Computational Mathematics. 42(6). 1287–1310. 8 indexed citations
8.
Chen, Dai-Qiang & Yan Zhou. (2015). Wavelet Frame Based Image Restoration via Combined Sparsity and Nonlocal Prior of Coefficients. Journal of Scientific Computing. 66(1). 196–224. 7 indexed citations
9.
Chen, Dai-Qiang, et al.. (2015). Primal–dual algorithm based on Gauss–Seidel scheme with application to multiplicative noise removal. Journal of Computational and Applied Mathematics. 292. 609–622. 7 indexed citations
10.
11.
Chen, Dai-Qiang. (2014). Regularized Generalized Inverse Accelerating Linearized Alternating Minimization Algorithm for Frame-Based Poissonian Image Deblurring. SIAM Journal on Imaging Sciences. 7(2). 716–739. 25 indexed citations
12.
Chen, Dai-Qiang & Lizhi Cheng. (2013). Fast linearized alternating direction minimization algorithm with adaptive parameter selection for multiplicative noise removal. Journal of Computational and Applied Mathematics. 257. 29–45. 13 indexed citations
13.
Chen, Dai-Qiang & Yan Zhou. (2013). Multiplicative Denoising Based on Linearized Alternating Direction Method Using Discrepancy Function Constraint. Journal of Scientific Computing. 60(3). 483–504. 14 indexed citations
14.
Chen, Dai-Qiang, et al.. (2013). A reduced l 2 – l 1 model with an alternating minimisation algorithm for support recovery of multiple measurement vectors. IET Signal Processing. 7(2). 112–119. 2 indexed citations
15.
Chen, Dai-Qiang, et al.. (2011). Spatially Adapted Total Variation Model to Remove Multiplicative Noise. IEEE Transactions on Image Processing. 21(4). 1650–1662. 45 indexed citations
16.
Chen, Dai-Qiang, Lizhi Cheng, & Fang Su. (2011). A New TV-Stokes Model with Augmented Lagrangian Method for Image Denoising and Deconvolution. Journal of Scientific Computing. 51(3). 505–526. 14 indexed citations
17.
Chen, Dai-Qiang & Lizhi Cheng. (2011). Spatially adapted regularization parameter selection based on the local discrepancy function for Poissonian image deblurring. Inverse Problems. 28(1). 15004–15004. 32 indexed citations
18.
Chen, Dai-Qiang, et al.. (2010). Restoration of images based on subspace optimization accelerating augmented Lagrangian approach. Journal of Computational and Applied Mathematics. 235(8). 2766–2774. 6 indexed citations
19.
Chen, Dai-Qiang & Lizhi Cheng. (2010). Alternative minimisation algorithm for non-local total variational image deblurring. IET Image Processing. 4(5). 353–364. 15 indexed citations
20.
Chen, Dai-Qiang. (2010). Nonlocal variational model and filter algorithm to remove multiplicative noise. Optical Engineering. 49(7). 77002–77002. 7 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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