Jun Tian
- Computer Vision and Pattern Recognition top 0.5%
- Signal Processing top 5%
- Computer Graphics and Computer-Aided Design top 5%
- Artificial Intelligence
- Information Systems top 10%
- Topics
- Image and Signal Denoising Methods (8 papers)Advanced Data Compression Techniques (8 papers)Advanced Steganography and Watermarking Techniques (8 papers)
- Cited by
- Computer Vision and Pattern RecognitionComputer Graphics and Computer-Aided DesignSignal Processing
- Journals
- IEEE Transactions on Image ProcessingExpert Systems with ApplicationsFrontiers in Immunology
- Partner nations
- United StatesChinaGermany
In The Last Decade
Jun Tian
26 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 68
- Computer Vision and Pattern Recognition 2.3k
- Signal Processing 132
- Computer Graphics and Computer-Aided Design 78
- Artificial Intelligence 73
- Information Systems 58
Countries citing papers authored by Jun Tian
This map shows the geographic impact of Jun Tian'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 Jun Tian with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Tian more than expected).
Fields of papers citing papers by Jun Tian
This network shows the impact of papers produced by Jun Tian. 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 Jun Tian. The network helps show where Jun Tian may publish in the future.
Co-authorship network of co-authors of Jun Tian
This figure shows the co-authorship network connecting the top 25 collaborators of Jun Tian. A scholar is included among the top collaborators of Jun Tian 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 Jun Tian. Jun Tian is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 3 | |
| 4 | 25 | |
| 5 | 2 | |
| 6 | 9 | |
| 7 | 2 | |
| 8 | 1 | |
| 9 | 38 | |
| 10 | 1 | |
| 11 | 2 | |
| 12 | Reversible data embedding using a difference expansionbreakdown → | 2091 |
| 13 | 78 | |
| 14 | 2 | |
| 15 | 6 | |
| 16 | 1 | |
| 17 | 4 | |
| 18 | 36 | |
| 19 | 6 | |
| 20 | 2 |
About Jun Tian
Jun Tian is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Media Technology, having authored 28 papers that have together received 2.4k indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (8 papers), Advanced Data Compression Techniques (8 papers) and Advanced Steganography and Watermarking Techniques (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.3k citations), Computer Graphics and Computer-Aided Design (78 citations) and Signal Processing (132 citations). Jun Tian has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Raymond O. Wells, Wenjun Zeng, Wang Qia, C.S. Burrus, Wei Dong, Yueqi Zhong, Kai Lü, Wei He, Jie Yu and Huiping Zhang. Their work appears in journals such as IEEE Transactions on Image Processing, Expert Systems with Applications and Frontiers in Immunology.
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.