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Topic · Computer Vision and Pattern Recognition

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. 9.8% 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%
5.8k
per year above
Top-10% rate
9.8%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+25%
the tick is no change
Largest producer
China
59% of the 10 largest, 2022-now
Image and Signal Denoising Methods

Which countries lead Image and Signal Denoising Methods research?

By volume, China and India publish the most (4.9k and 1.3k works in 2022-now).

By volume, 2022-now

  1. 1 China 4.9k works
  2. 2 India 1.3k works
  3. 3 United States 732 works
  4. 4 South Korea 246 works
  5. 5 Japan 224 works
  6. 6 France 197 works
  7. 7 Germany 183 works
  8. 8 United Kingdom 180 works
  9. 9 Russia 167 works
  10. 10 Italy 150 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.

China: 59.2%India: 15.4%United States: 8.9%South Korea: 3.0%6 others listed: 13.4%59%largest
China4,852 · 59.2%India1,261 · 15.4%United States732 · 8.9%South Korea246 · 3.0%6 others listed1,100 · 13.4%

Shares of the rows listed above, not of the whole node.

Which institutions lead Image and Signal Denoising Methods research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Image and Signal Denoising Methods research, followed by Xidian University and University of Electronic Science and Technology of China.

By volume, 2022-now

  1. 1 Chinese Academy of SciencesChina 173 works
  2. 2 Xidian UniversityChina 85 works
  3. 3 University of Electronic Science and Technology of ChinaChina 84 works
  4. 4 Harbin Institute of TechnologyChina 81 works
  5. 5 Beijing Institute of TechnologyChina 74 works
  6. 6 Northwestern Polytechnical UniversityChina 70 works
  7. 7 Wuhan UniversityChina 69 works
  8. 8 Centre National de la Recherche ScientifiqueFrance 69 works
  9. 9 Shanghai Jiao Tong UniversityChina 69 works
  10. 10 Xi'an Jiaotong UniversityChina 65 works

Who are the leading researchers in Image and Signal Denoising Methods?

The most-cited researchers publishing on Image and Signal Denoising Methods include Kaiming He, Yoshua Bengio and Geoffrey E. Hinton.

  1. 1 Kaiming He Israel 562k citations
  2. 2 Yoshua Bengio Canada 476k citations
  3. 3 Geoffrey E. Hinton Canada 454k citations
  4. 4 Robert John Tibshirani United States 396k citations
  5. 5 Xiangyu Zhang 327k citations
  6. 6 Karl John Friston United Kingdom 310k citations
  7. 7 Trevor Hastie United States 301k citations
  8. 8 Xiaogang Wang China 260k citations
  9. 9 Yann LeCun United States 256k citations
  10. 10 Andrew Zisserman United Kingdom 255k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Image and Signal Denoising Methods?

The most cited paper in Image and Signal Denoising Methods is "A Computational Approach to Edge Detection" (John Canny, 1986), with 29,563 citations. Next come "Compressed sensing" (23,533) and "Pattern Recognition and Machine Learning" (22,033).

Most cited papers
#PaperReleasedCitations
1 A Computational Approach to Edge Detection John Canny · IEEE Transactions on Pattern Analysis and Machine Intelligence Nov 1986 29,563
2 Compressed sensing David L. Donoho · IEEE Transactions on Information Theory Apr 2006 23,533
3 Pattern Recognition and Machine Learning Nasser M. Nasrabadi · Journal of Electronic Imaging 2007 22,033
4 A theory for multiresolution signal decomposition: the wavelet representation Stéphane Mallat · IEEE Transactions on Pattern Analysis and Machine Intelligence Jul 1989 21,434
5 Reducing the Dimensionality of Data with Neural Networks Geoffrey E. Hinton, Ruslan Salakhutdinov · Science 27 Jul 2006 21,368
All 98 most cited papers, with filters and sorting

Where is Image and Signal Denoising Methods research done?

The largest centres of Image and Signal Denoising Methods research in 2022-now 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-now

  1. 1 BeijingChina 807 works
  2. 2 Xi'anChina 358 works
  3. 3 ShanghaiChina 292 works
  4. 4 NanjingChina 256 works
  5. 5 WuhanChina 243 works
  6. 6 ChengduChina 223 works
  7. 7 GuangzhouChina 175 works
  8. 8 HangzhouChina 142 works
  9. 9 HarbinChina 142 works
  10. 10 ShenzhenChina 134 works

Where it is the local speciality

  1. PatnaIN · 34.1 works8.2×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Image and Signal Denoising Methods than the world average.

See Image and Signal Denoising Methods on the map

Where is the best place to study Image and Signal Denoising Methods?

Among universities, judged by research, Wuhan University, University of Science and Technology of China and Northwestern Polytechnical University 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.

0%20%40%mean 23.89%fractional works in this field (log) →share of work in the world top 10% →Nanyang Technologica…Wuhan UniversityUniversity of Scienc…Northwestern Polytec…
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Wuhan UniversityChina 82.026.2%5.6×69 +30.8%
2University of Science and Technology of ChinaChina 78.723.3%5.9×61 +94.5%
3Northwestern Polytechnical UniversityChina 76.322.6%7.1×70 +62.6%
4Nanyang Technological UniversitySingapore 74.131.7%3.4×23 +75.3%
5Chinese University of Hong KongChina 73.628.3%2.5×16 +46.1%
6Hunan UniversityChina 72.627.3%4.2×26 +46.2%
7Xidian UniversityChina 71.715.7%13.5×85 +9.3%
8Hong Kong Polytechnic UniversityChina 71.227.4%2.8×21 +52.4%
9Harbin Institute of TechnologyChina 70.614.1%5.7×81 +59.4%
10Peking UniversityChina 69.122.3%2.3×41 +44.4%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Image and Signal Denoising Methods, country by country

Is Image and Signal Denoising Methods research growing?

Output in 2022-2025 was 25% higher than in 2018-2021, peaking in 2002. The fastest-growing topics are Image and Signal Denoising Methods.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Image and Signal Denoising Methods as a percentage of every work published that year, 0.053% in 2024.

still counting0.10%0.20%Image and Signal Denoising Methods0.043% in 202519801990200020102020

Fractional works of Image and Signal Denoising Methods over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Image and Signal Denoising Methods9.8% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

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.

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