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Advanced Image Fusion Techniques

Advanced Image Fusion Techniques is a research topic within Media Technology. Science Explorer counts 29k research works in it since 1952. 19.1% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the fusion of multispectral and hyperspectral images using techniques such as wavelet transform, sparse representation, convolutional neural networks, and pansharpening. The research covers methods for remote sensing, image quality assessment, and applications in fields such as medical imaging.

  • Image Fusion
  • Multispectral
  • Hyperspectral
  • Wavelet Transform
  • Sparse Representation
  • Convolutional Neural Network
  • Pansharpening
  • Remote Sensing
  • Directional Transform
  • Quality Metric
Research works
29k
fractional, since 1952
In the world top 10%
5.5k
per year above
Top-10% rate
19.1%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+44%
the tick is no change

Which countries lead Advanced Image Fusion Techniques research?

By volume, China and India publish the most (4.9k and 924 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 4.9k works
  2. 2 India 924 works
  3. 3 United States 281 works
  4. 4 South Korea 113 works
  5. 5 Russia 106 works
  6. 6 France 94 works
  7. 7 United Kingdom 92 works
  8. 8 Japan 84 works
  9. 9 Türkiye 78 works
  10. 10 ?? 77 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: 72.5%India: 13.7%United States: 4.2%South Korea: 1.7%6 others listed: 7.9%73%largest
China4,886 · 72.5%India924 · 13.7%United States281 · 4.2%South Korea113 · 1.7%6 others listed531 · 7.9%

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

Which institutions lead Advanced Image Fusion Techniques research?

By volume in 2022–2025, Xidian University publishes the most Advanced Image Fusion Techniques research, followed by Wuhan University and Chinese Academy of Sciences.

Who are the leading researchers in Advanced Image Fusion Techniques?

The most-cited researchers publishing on Advanced Image Fusion Techniques include Wei Liu, Luc Van Gool and Ming–Hsuan Yang.

  1. 1 Wei Liu China 9.7k citations
  2. 2 Luc Van Gool Switzerland 8.5k citations
  3. 3 Ming–Hsuan Yang United States 7.4k citations
  4. 4 Dacheng Tao Australia 6.1k citations
  5. 5 Lei Zhang Hong Kong 5.3k citations
  6. 6 Richard G. Baraniuk United States 4.4k citations
  7. 7 Jinde Cao China 4.2k citations

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

Where is Advanced Image Fusion Techniques research done?

The largest centres of Advanced Image Fusion Techniques research in 2022–2025 are Beijing (China), Xi'an (China), Wuhan (China) and Nanjing (China). Among places with at least 20 works in it, it is an unusually large share of all research in Yantai, Xi'an and Patna.

Largest cities, 2022–2025

  1. 1 Beijing China 795 works
  2. 2 Xi'an China 414 works
  3. 3 Wuhan China 299 works
  4. 4 Nanjing China 274 works
  5. 5 Shanghai China 239 works
  6. 6 Chengdu China 171 works
  7. 7 Changsha China 133 works
  8. 8 Chongqing China 131 works
  9. 9 Guangzhou China 131 works
  10. 10 Harbin China 125 works

Where it is the local speciality

  1. YantaiCN · 39.5 works9.1×
  2. Xi'anCN · 414.4 works7.0×
  3. PatnaIN · 21.4 works6.8×
  4. KunmingCN · 91.8 works6.0×
← less than its size predictsmore →

Location quotient: how much more of its research is in Advanced Image Fusion Techniques than the world average.

See Advanced Image Fusion Techniques on the map

Where is the best place to study Advanced Image Fusion Techniques?

Among universities, judged by research, Wuhan University, Xidian University 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 30.15%fractional works in this node (log) →share in the world top 10% →Wuhan University: 98, 45.3%Xidian University: 116, 31.9%Northwestern Polytechnical University: 86, 35.4%Nanjing University of Information Science and Technology: 31, 26.5%Dalian Maritime University: 40, 27.0%Aerospace Information Research Institute: 36, 37.2%Yunnan University: 44, 20.2%Nanjing University of Science and Technology: 68, 23.7%Beijing Institute of Technology: 78, 27.7%China University of Geosciences: 38, 26.6%Wuhan UniversityNorthwestern Polytec…Xidian UniversityNanjing University o…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Wuhan UniversityChina 78.445.3%10.7×98 +70.5%
2 Xidian UniversityChina 71.631.9%21.0×116 +66.5%
3 Northwestern Polytechnical UniversityChina 68.935.4%10.0×86 +22.5%
4 Nanjing University of Information Science and TechnologyChina 68.026.5%10.2×31 +212.6%
5 Dalian Maritime UniversityChina 67.027.0%14.6×40 +178.0%
6 Aerospace Information Research InstituteChina 64.937.2%40.6×36
7 Yunnan UniversityChina 62.920.2%15.4×44 +150.5%
8 Nanjing University of Science and TechnologyChina 61.123.7%11.0×68 +41.5%
9 Beijing Institute of TechnologyChina 60.127.7%8.7×78 +26.6%
10 China University of GeosciencesChina 59.726.6%11.0×38 +37.3%

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 Advanced Image Fusion Techniques research growing?

Output in 2018–2022 was 44% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Advanced Image Fusion Techniques.

19801990200020102020
grewheldshrank

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.