Hao Fang

1.5k total citations · 1 hit paper
55 papers, 1.1k citations indexed

About

Hao Fang is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Computer Networks and Communications. According to data from OpenAlex, Hao Fang has authored 55 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Computer Vision and Pattern Recognition, 19 papers in Media Technology and 18 papers in Computer Networks and Communications. Recurrent topics in Hao Fang's work include Advanced Image Fusion Techniques (14 papers), Image and Signal Denoising Methods (13 papers) and Caching and Content Delivery (10 papers). Hao Fang is often cited by papers focused on Advanced Image Fusion Techniques (14 papers), Image and Signal Denoising Methods (13 papers) and Caching and Content Delivery (10 papers). Hao Fang collaborates with scholars based in China, United States and Germany. Hao Fang's co-authors include T. V. Lakshman, Zhenghua Huang, Tianxu Zhang, Sarit Mukherjee, Murali Kodialam, Ramana Rao Kompella, Advait Dixit, Haoyu Song, Zhi-Li Zhang and Yaozong Zhang and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Geoscience and Remote Sensing and International Journal of Remote Sensing.

In The Last Decade

Hao Fang

53 papers receiving 1.0k citations

Hit Papers

TMSF: Taylor expansion approximation network with multi-s... 2025 2026 2025 5 10 15 20 25
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. TMSF: Taylor expansion approximation network with multi-stage feature representation for optical flow estimation
    2025 27 citations Zhenghua Huang, Wenhao Hu et al. Digital Signal Processing profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hao Fang China 20 484 383 224 161 131 55 1.1k
Dinesh Rajan United States 16 485 1.0× 213 0.6× 94 0.4× 603 3.7× 50 0.4× 141 1.3k
Markus Steinberger Austria 21 236 0.5× 844 2.2× 91 0.4× 40 0.2× 166 1.3× 86 1.4k
Zhiping Jiang China 20 399 0.8× 249 0.7× 285 1.3× 976 6.1× 170 1.3× 66 1.4k
C.C. Ko Singapore 17 324 0.7× 512 1.3× 315 1.4× 347 2.2× 76 0.6× 131 1.5k
Gabriel Falcão Portugal 18 544 1.1× 219 0.6× 86 0.4× 624 3.9× 175 1.3× 106 1.1k
Nicolás Guil Spain 16 107 0.2× 734 1.9× 106 0.5× 103 0.6× 146 1.1× 87 1.0k
Saeid Nooshabadi United States 17 248 0.5× 298 0.8× 68 0.3× 516 3.2× 139 1.1× 164 986
Bin Tang China 17 709 1.5× 182 0.5× 27 0.1× 340 2.1× 275 2.1× 132 1.2k
Takuro Sato Japan 14 467 1.0× 124 0.3× 40 0.2× 611 3.8× 199 1.5× 90 979
Carlos Pomalaza‐Ráez United States 21 491 1.0× 162 0.4× 124 0.6× 593 3.7× 55 0.4× 108 1.3k

Countries citing papers authored by Hao Fang

Since Specialization
Citations

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

Fields of papers citing papers by Hao Fang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hao Fang

This figure shows the co-authorship network connecting the top 25 collaborators of Hao Fang. A scholar is included among the top collaborators of Hao Fang 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 Hao Fang. Hao Fang 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.
Fang, Hao, et al.. (2026). Auxiliary Model Hierarchical Generalized Extended Recursive Parameter Estimation for Autoregressive Output‐Error Autoregressive Moving Average Systems. Optimal Control Applications and Methods. 47(2). 437–449. 4 indexed citations
2.
Huang, Zhenghua, et al.. (2025). TMSF: Taylor expansion approximation network with multi-stage feature representation for optical flow estimation. Digital Signal Processing. 162. 105157–105157. 27 indexed citations breakdown →
3.
Xia, Menghan, et al.. (2025). DSAFusion: Detail-semantic-aware network for infrared and low-light visible image fusion. Infrared Physics & Technology. 147. 105804–105804. 3 indexed citations
4.
Huang, Zhenghua, Xi Li, Yaozong Zhang, et al.. (2025). DMSDA-YOLO: Dynamic Multiscale Dilated Attention for Remote Sensing Object Detection. IEEE Geoscience and Remote Sensing Letters. 22. 1–5.
5.
Huang, Zhenghua, Haoyong Yu, Qian Li, et al.. (2025). RCST: Residual Context-Sharing Transformer Cascade to Approximate Taylor Expansion for Remote Sensing Image Denoising. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–15. 4 indexed citations
6.
Cheng, Yong, et al.. (2024). A Context-Aware Edge-Cloud Collaboration Framework for QoS Prediction. Tsinghua Science & Technology. 30(3). 1201–1214. 2 indexed citations
7.
8.
Li, Shaoyi, et al.. (2023). MSPIF: Multi-stage progressive visible and infrared image fusion with structures preservation. Infrared Physics & Technology. 133. 104848–104848. 9 indexed citations
9.
Huang, Zhenghua, et al.. (2023). D3CNNs: Dual Denoiser Driven Convolutional Neural Networks for Mixed Noise Removal in Remotely Sensed Images. Remote Sensing. 15(2). 443–443. 6 indexed citations
10.
11.
Huang, Zhenghua, et al.. (2023). MD3: Model-Driven Deep Remotely Sensed Image Denoising. Remote Sensing. 15(2). 445–445. 7 indexed citations
12.
Gao, Liang, et al.. (2023). Bandpass Filter Based Dual-stream Network for Face Anti-spoofing. 6403–6410. 1 indexed citations
13.
An, Qing, et al.. (2022). Blind Additive Gaussian White Noise Level Estimation from a Single Image by Employing Chi-Square Distribution. Entropy. 24(11). 1518–1518. 7 indexed citations
14.
Chang, Hyunseok, Matteo Varvello, Hao Fang, & Sarit Mukherjee. (2022). A Tale of Three Videoconferencing Applications: Zoom, Webex, and Meet. IEEE/ACM Transactions on Networking. 30(5). 2343–2358. 6 indexed citations
15.
Wang, Lei, et al.. (2022). Road surface state recognition using deep convolution network on the low-power-consumption embedded device. Microprocessors and Microsystems. 96. 104740–104740. 6 indexed citations
16.
Huang, Zhenghua, Zhicheng Wang, Yaozong Zhang, et al.. (2022). DLRP: Learning Deep Low-Rank Prior for Remotely Sensed Image Denoising. IEEE Geoscience and Remote Sensing Letters. 19. 1–5. 25 indexed citations
17.
Huang, Zhenghua, et al.. (2019). Robust contact-point detection from pantograph-catenary infrared images by employing horizontal-vertical enhancement operator. Infrared Physics & Technology. 101. 146–155. 51 indexed citations
18.
Fang, Hao, T. V. Lakshman, Sarit Mukherjee, & Haoyu Song. (2010). Secure cloud computing with a virtualized network infrastructure. IEEE International Conference on Cloud Computing Technology and Science. 16–16. 35 indexed citations
19.
Fang, Hao, Murali Kodialam, & T. V. Lakshman. (2007). Building high accuracy bloom filters using partitioned hashing. ACM SIGMETRICS Performance Evaluation Review. 35(1). 277–288. 20 indexed citations
20.
Fang, Hao, Murali Kodialam, T. V. Lakshman, & Hui Zhang. (2005). Fast payload-based flow estimation for traffic monitoring and network security. 211–220. 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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