Rencan Nie

2.8k total citations
128 papers, 2.1k citations indexed

About

Rencan Nie is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Aerospace Engineering. According to data from OpenAlex, Rencan Nie has authored 128 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 92 papers in Computer Vision and Pattern Recognition, 89 papers in Media Technology and 17 papers in Aerospace Engineering. Recurrent topics in Rencan Nie's work include Advanced Image Fusion Techniques (85 papers), Image Enhancement Techniques (52 papers) and Remote-Sensing Image Classification (43 papers). Rencan Nie is often cited by papers focused on Advanced Image Fusion Techniques (85 papers), Image Enhancement Techniques (52 papers) and Remote-Sensing Image Classification (43 papers). Rencan Nie collaborates with scholars based in China, South Korea and Bahrain. Rencan Nie's co-authors include Dongming Zhou, Jinde Cao, Kangjian He, Xin Jin, Ruichao Hou, Shaowen Yao, Xiaopeng Guo, Yanbu Guo, Qian Jiang and Wenhua Qian and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, Expert Systems with Applications and IEEE Access.

In The Last Decade

Rencan Nie

121 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rencan Nie China 24 1.5k 1.3k 379 209 148 128 2.1k
Guobao Xiao China 22 486 0.3× 1.1k 0.8× 502 1.3× 69 0.3× 298 2.0× 91 1.8k
Yongchao Xu China 16 594 0.4× 1.5k 1.1× 264 0.7× 106 0.5× 313 2.1× 44 1.8k
Weilin Huang China 24 687 0.5× 2.6k 2.0× 204 0.5× 134 0.6× 840 5.7× 47 3.1k
Mohammad Haghighat United States 10 683 0.5× 856 0.6× 188 0.5× 153 0.7× 137 0.9× 18 1.4k
Shohreh Kasaei Iran 19 313 0.2× 1.4k 1.0× 165 0.4× 176 0.8× 494 3.3× 167 1.9k
Hongzhi Zhang China 23 432 0.3× 1.0k 0.8× 91 0.2× 264 1.3× 242 1.6× 100 1.9k
Ioannis Pratikakis Greece 37 978 0.7× 3.9k 3.0× 270 0.7× 52 0.2× 611 4.1× 130 4.7k
Jorge Sánchez Argentina 9 465 0.3× 2.5k 1.9× 460 1.2× 141 0.7× 712 4.8× 20 2.9k
Mehran Yazdi Iran 20 379 0.3× 877 0.7× 89 0.2× 283 1.4× 240 1.6× 113 1.6k

Countries citing papers authored by Rencan Nie

Since Specialization
Citations

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

Fields of papers citing papers by Rencan Nie

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rencan Nie

This figure shows the co-authorship network connecting the top 25 collaborators of Rencan Nie. A scholar is included among the top collaborators of Rencan Nie 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 Rencan Nie. Rencan Nie 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.
Nie, Rencan, et al.. (2024). HP-CRL: High-resolution preservation driven collaborative representation learning for infrared and visible image fusion. Optics & Laser Technology. 177. 111184–111184. 2 indexed citations
2.
Wang, Chengchao, et al.. (2024). FCLFusion: A frequency-aware and collaborative learning for infrared and visible image fusion. Engineering Applications of Artificial Intelligence. 137. 109192–109192. 5 indexed citations
3.
Nie, Rencan, et al.. (2024). IMQFusion: Infrared and visible image fusion via implicit multi-resolution preservation and query aggregation. Expert Systems with Applications. 257. 125014–125014. 2 indexed citations
4.
Nie, Rencan, et al.. (2024). LRFE-CL: A self-supervised fusion network for infrared and visible image via low redundancy feature extraction and contrastive learning. Expert Systems with Applications. 251. 124125–124125. 2 indexed citations
5.
Zhou, Dongming, et al.. (2024). Focus-aware and deep restoration network with transformer for multi-focus image fusion. Digital Signal Processing. 149. 104473–104473. 3 indexed citations
6.
7.
Nie, Rencan, et al.. (2024). A Deep Multiresolution Representation Framework for Pansharpening. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–16. 7 indexed citations
8.
Zhou, Dongming, et al.. (2023). Robust multi-focus image fusion using focus property detection and deep image matting. Expert Systems with Applications. 237. 121389–121389. 5 indexed citations
9.
Nie, Rencan, et al.. (2023). MPEFNet: Multilevel Progressive Enhancement Fusion Network for Pansharpening. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 16. 9358–9368. 4 indexed citations
10.
Nie, Rencan, et al.. (2023). A Fractional-Order Variation With a Novel Norm to Fuse Infrared and Visible Images. IEEE Transactions on Instrumentation and Measurement. 72. 1–12. 8 indexed citations
11.
Liu, Yanyu, et al.. (2023). An Improved Hybrid Network With a Transformer Module for Medical Image Fusion. IEEE Journal of Biomedical and Health Informatics. 27(7). 3489–3500. 23 indexed citations
12.
Nie, Rencan, et al.. (2023). IE-CFRN: Information exchange-based collaborative feature representation network for multi-modal medical image fusion. Biomedical Signal Processing and Control. 86. 105301–105301. 7 indexed citations
13.
Liu, Yanyu, et al.. (2022). TSE_Fuse: Two stage enhancement method using attention mechanism and feature-linking model for infrared and visible image fusion. Digital Signal Processing. 123. 103387–103387. 12 indexed citations
14.
Zhou, Dongming, et al.. (2022). EDAfuse: A encoder–decoder with atrous spatial pyramid network for infrared and visible image fusion. IET Image Processing. 17(1). 132–143. 4 indexed citations
15.
Liu, Yanyu, et al.. (2022). Asymmetric Global–Local Mutual Integration Network for RGBT Tracking. IEEE Transactions on Instrumentation and Measurement. 71. 1–17. 22 indexed citations
16.
Wang, Xue, et al.. (2022). CEFusion: Multi‐Modal medical image fusion via cross encoder. IET Image Processing. 16(12). 3177–3189. 9 indexed citations
17.
Nie, Rencan, et al.. (2021). A Total Variation With Joint Norms For Infrared and Visible Image Fusion. IEEE Transactions on Multimedia. 24. 1460–1472. 35 indexed citations
18.
Cao, Jinde, et al.. (2020). CNN-Based Embroidery Style Rendering. International Journal of Pattern Recognition and Artificial Intelligence. 34(14). 2059045–2059045. 11 indexed citations
19.
Liu, Yanyu, Ruichao Hou, Dongming Zhou, et al.. (2020). Multimodal medical image fusion based on the spectral total variation and local structural patch measurement. International Journal of Imaging Systems and Technology. 31(1). 391–411. 11 indexed citations
20.
Zhou, Dongming, et al.. (2009). Color image segmentation and edge detection using Unit-Linking PCNN and image entropy. Computer Engineering and Applications Journal. 45(12). 2 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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