Hailin Wang

740 total citations · 1 hit paper
33 papers, 463 citations indexed

About

Hailin Wang is a scholar working on Computational Mechanics, Computer Vision and Pattern Recognition and Computational Mathematics. According to data from OpenAlex, Hailin Wang has authored 33 papers receiving a total of 463 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computational Mechanics, 15 papers in Computer Vision and Pattern Recognition and 11 papers in Computational Mathematics. Recurrent topics in Hailin Wang's work include Sparse and Compressive Sensing Techniques (15 papers), Image and Signal Denoising Methods (12 papers) and Tensor decomposition and applications (11 papers). Hailin Wang is often cited by papers focused on Sparse and Compressive Sensing Techniques (15 papers), Image and Signal Denoising Methods (12 papers) and Tensor decomposition and applications (11 papers). Hailin Wang collaborates with scholars based in China, Macao and Qatar. Hailin Wang's co-authors include Jianjun Wang, Jiangjun Peng, Deyu Meng, Feng Zhang, Tingwen Huang, Xiangyong Cao, Xin Luo, Jianwen Huang, Guangzhi Zhu and A. G. Kozlov and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Cleaner Production and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Hailin Wang

30 papers receiving 447 citations

Hit Papers

Guaranteed Tensor Recovery Fused Low-rankness and Smoothness 2023 2026 2024 2025 2023 25 50 75
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. Guaranteed Tensor Recovery Fused Low-rankness and Smoothness
    2023 79 citations Hailin Wang, Jiangjun Peng et al. profile →

Peers

Hailin Wang
Xiujun Zhang China
Weihong Guo United States
Ruomei Yan United Kingdom
Martin Landmann Germany
Yechao Bai China
Kaixuan Wei China
Fangyin Wei United States
Xiujun Zhang China
Hailin Wang
Citations per year, relative to Hailin Wang Hailin Wang (= 1×) peers Xiujun Zhang

Countries citing papers authored by Hailin Wang

Since Specialization
Citations

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

Fields of papers citing papers by Hailin Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hailin Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Hailin Wang. A scholar is included among the top collaborators of Hailin Wang 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 Hailin Wang. Hailin Wang 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.
Wang, Hailin, Feng Zhang, Jianjun Wang, et al.. (2025). Hyperspectral Anomaly Detection Fused Unified Nonconvex Tensor Ring Factors Regularization. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–21.
2.
Hou, Jingyao, et al.. (2024). Tensor recovery from binary measurements fused low-rankness and smoothness. Signal Processing. 221. 109480–109480. 5 indexed citations
3.
Shu, Hao, Hailin Wang, Jiangjun Peng, & Deyu Meng. (2024). Low-Rank Tensor Completion With 3-D Spatiotemporal Transform for Traffic Data Imputation. IEEE Transactions on Intelligent Transportation Systems. 25(11). 18673–18687. 8 indexed citations
4.
Wang, Hailin, et al.. (2024). Tensor Ring Decomposition-Based Generalized and Efficient Nonconvex Approach for Hyperspectral Anomaly Detection. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–18. 3 indexed citations
5.
Wang, Hailin, et al.. (2024). Nonconvex Robust High-Order Tensor Completion Using Randomized Low-Rank Approximation. IEEE Transactions on Image Processing. 33. 2835–2850. 11 indexed citations
6.
Zhang, Wenhao, et al.. (2024). A novel method for detecting missing seedlings based on UAV images and rice transplanter operation information. Computers and Electronics in Agriculture. 229. 109789–109789. 4 indexed citations
7.
Wang, Hailin, et al.. (2024). Hyperspectral image restoration via RPCA model based on spectral-spatial correlated total variation regularizer. Neurocomputing. 619. 128885–128885. 1 indexed citations
8.
Liu, Pei, Jiangjun Peng, Hailin Wang, Danfeng Hong, & Xiangyong Cao. (2024). Infrared Small Target Detection via Joint Low Rankness and Local Smoothness Prior. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–15. 4 indexed citations
9.
Wang, Hailin, et al.. (2024). Exploring skid resistance over time: Steel slag as a pavement aggregate—comparative study and morphological analysis. Journal of Cleaner Production. 464. 142779–142779. 24 indexed citations
10.
11.
Peng, Xiao, et al.. (2024). A Continuous Authentication Method Based on Improved Sparrow Search Algorithm and XGBoost. 242–250. 1 indexed citations
12.
Hou, Jingyao, et al.. (2023). Tensor Compressive Sensing Fused Low-Rankness and Local-Smoothness. Proceedings of the AAAI Conference on Artificial Intelligence. 37(7). 8879–8887. 11 indexed citations
13.
Chen, Ge, et al.. (2023). Fluorescence microscopy images denoising via deep convolutional sparse coding. Signal Processing Image Communication. 117. 117003–117003. 2 indexed citations
14.
Wang, Hailin, et al.. (2023). Generalized nonconvex regularization for tensor RPCA and its applications in visual inpainting. Applied Intelligence. 53(20). 23124–23146. 5 indexed citations
15.
Wang, Hailin, et al.. (2022). Low-Rank High-Order Tensor Completion With Applications in Visual Data. IEEE Transactions on Image Processing. 31. 2433–2448. 81 indexed citations
16.
Huang, Jianwen, et al.. (2022). An analysis of noise folding for low-rank matrix recovery. Analysis and Applications. 21(2). 429–451. 2 indexed citations
17.
Wang, Hailin, et al.. (2022). Robust High-Order Tensor Recovery Via Nonconvex Low-Rank Approximation. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 16. 3633–3637. 3 indexed citations
18.
Wang, Hailin, et al.. (2021). Generalized Nonconvex Approach for Low-Tubal-Rank Tensor Recovery. IEEE Transactions on Neural Networks and Learning Systems. 33(8). 3305–3319. 64 indexed citations
19.
Wang, Hailin, et al.. (2020). Estimating Structural Missing Values Via Low-Tubal-Rank Tensor Completion. 3297–3301. 8 indexed citations
20.
Yan, Dapeng, et al.. (1993). Study of optical visualization methods for exhaust jet of rocket engine. Journal of Aerospace Power. 8(1). 1–5. 1 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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