Image and Signal Denoising Methods
Image and Signal Denoising Methods is a research topic within Computer Vision and Pattern Recognition. Science Explorer counts 59k research works in it since 1950. 14.4% of them reached the world's top 10% most cited for their field and year.
This cluster of papers encompasses a wide range of techniques and algorithms for image denoising, including sparse representations, wavelet transform, deep learning with convolutional neural networks, non-local means, and methods specific to handling different types of noise such as Gaussian, Poisson, and salt-and-pepper noise. The applications also extend to hyperspectral imaging and the use of anisotropic diffusion for speckle reduction.
- Image Denoising
- Sparse Representations
- Wavelet Transform
- Deep Learning
- Non-Local Means
- Hyperspectral Imaging
- Gaussian Noise
- Anisotropic Diffusion
- Poisson Noise
- Convolutional Neural Networks
- Research works
- 59k fractional, since 1950
- In the world top 10%
- 8.5k per year above
- Top-10% rate
- 14.4% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +5% the tick is no change
Which countries lead Image and Signal Denoising Methods research?
By volume, China and India publish the most (4.5k and 1.2k works in 2022–2025).
By volume, 2022–2025
- 1 China 4.5k works
- 2 India 1.2k works
- 3 United States 705 works
- 4 South Korea 236 works
- 5 Japan 215 works
- 6 France 193 works
- 7 Germany 176 works
- 8 United Kingdom 174 works
- 9 Russia 162 works
- 10 Italy 145 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Image and Signal Denoising Methods research?
By volume in 2022–2025, Xidian University publishes the most Image and Signal Denoising Methods research, followed by University of Electronic Science and Technology of China and Harbin Institute of Technology.
By volume, 2022–2025
- 1 Xidian UniversityChina 81 works
- 2 University of Electronic Science and Technology of ChinaChina 79 works
- 3 Harbin Institute of TechnologyChina 79 works
- 4 Beijing Institute of TechnologyChina 71 works
- 5 Northwestern Polytechnical UniversityChina 67 works
- 6 Wuhan UniversityChina 66 works
- 7 Xi'an Jiaotong UniversityChina 63 works
- 8 Shanghai Jiao Tong UniversityChina 63 works
- 9 Chinese Academy of SciencesChina 62 works
- 10 University of Science and Technology of ChinaChina 59 works
Who are the leading researchers in Image and Signal Denoising Methods?
The most-cited researchers publishing on Image and Signal Denoising Methods include Xiaogang Wang, Wei Liu and H. Vincent Poor.
- 1 Xiaogang Wang Russia 13k citations
- 2 Wei Liu China 9.7k citations
- 3 H. Vincent Poor United States 9.5k citations
- 4 Luc Van Gool Switzerland 8.5k citations
- 5 Alan Yuille United States 8.4k citations
- 6 Stanley Osher United States 8k citations
- 7 Xiaoou Tang Hong Kong 8k citations
- 8 Chen Change Loy Singapore 7.7k citations
- 9 Jiaya Jia Hong Kong 7.5k citations
- 10 Ming–Hsuan Yang United States 7.4k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Image and Signal Denoising Methods research done?
The largest centres of Image and Signal Denoising Methods research in 2022–2025 are Beijing (China), Xi'an (China), Shanghai (China) and Nanjing (China). Among places with at least 20 works in it, it is an unusually large share of all research in Patna.
Largest cities, 2022–2025
Where it is the local speciality
- PatnaIN · 32.1 works8.2×
Location quotient: how much more of its research is in Image and Signal Denoising Methods than the world average.
Where is the best place to study Image and Signal Denoising Methods?
Among universities, judged by research, Islamic University of Science and Technology, Xidian University and Aerospace Information Research Institute score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Islamic University of Science and TechnologyIndia | 62.6 | 43.6% | 34.8× | 11 | — |
| 2 | Xidian UniversityChina | 62.4 | 21.1% | 11.8× | 81 | -6.7% |
| 3 | Aerospace Information Research InstituteChina | 61.6 | 37.4% | 13.9× | 15 | — |
| 4 | Wuhan UniversityChina | 59.7 | 34.4% | 5.8× | 66 | +28.2% |
| 5 | Northwestern Polytechnical UniversityChina | 56.7 | 31.3% | 6.3× | 68 | -14.3% |
| 6 | University of Electronic Science and Technology of ChinaChina | 56.1 | 18.2% | 7.5× | 79 | +31.7% |
| 7 | Beijing Institute of TechnologyChina | 55.1 | 26.4% | 6.4× | 71 | +4.2% |
| 8 | University of MacauMacau | 54.7 | 39.1% | 4.4× | 13 | +60.5% |
| 9 | Indian Institute of Technology PatnaIndia | 53.8 | 23.7% | 14.3× | 18 | — |
| 10 | Shandong Institute of Business and TechnologyChina | 53.2 | 27.3% | 16.6× | 13 | +57.1% |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Image and Signal Denoising Methods research growing?
Output in 2018–2022 was 5% higher than in 2013–2017, peaking in 2002. The fastest-growing topics are Image and Signal Denoising Methods.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.