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Topic · Computer Vision and Pattern Recognition

Image Enhancement Techniques

Image Enhancement Techniques is a research topic within Computer Vision and Pattern Recognition. Science Explorer counts 28k research works in it since 1950. 9.7% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the advancements in image enhancement techniques, including dehazing, contrast enhancement, and color transfer. It covers a wide range of topics such as underwater imaging, single image restoration, low-light enhancement, and high dynamic range imaging. The cluster showcases the application of deep learning methods in addressing challenges related to image processing and enhancement.

  • Dehazing
  • Contrast Enhancement
  • Image Processing
  • Underwater Imaging
  • Single Image Restoration
  • Low-Light Enhancement
  • Deep Learning
  • Haze Removal
  • Color Transfer
  • High Dynamic Range
Research works
28k
fractional, since 1950
In the world top 10%
2.7k
per year above
Top-10% rate
9.7%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+51%
the tick is no change
Largest producer
China
69% of the 10 largest, 2022-now
Image Enhancement Techniques

Which countries lead Image Enhancement Techniques research?

By volume, China and India publish the most (5.9k and 1.1k works in 2022-now).

By volume, 2022-now

  1. 1 China 5.9k works
  2. 2 India 1.1k works
  3. 3 United States 470 works
  4. 4 South Korea 281 works
  5. 5 Japan 212 works
  6. 6 United Kingdom 168 works
  7. 7 Taiwan 141 works
  8. 8 Germany 105 works
  9. 9 ?? 105 works
  10. 10 Canada 91 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: 69.1%India: 12.4%United States: 5.5%South Korea: 3.3%6 others listed: 9.7%69%largest
China5,878 · 69.1%India1,052 · 12.4%United States470 · 5.5%South Korea281 · 3.3%6 others listed821 · 9.7%

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

Which institutions lead Image Enhancement Techniques research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Image Enhancement Techniques research, followed by Wuhan University and Beijing Institute of Technology.

By volume, 2022-now

  1. 1 Chinese Academy of SciencesChina 197 works
  2. 2 Wuhan UniversityChina 72 works
  3. 3 Beijing Institute of TechnologyChina 72 works
  4. 4 Northwestern Polytechnical UniversityChina 71 works
  5. 5 Dalian Maritime UniversityChina 70 works
  6. 6 University of Science and Technology of ChinaChina 70 works
  7. 7 Shanghai Jiao Tong UniversityChina 69 works
  8. 8 Xidian UniversityChina 65 works
  9. 9 Nanjing University of Science and TechnologyChina 61 works
  10. 10 Zhejiang UniversityChina 57 works

Who are the leading researchers in Image Enhancement Techniques?

The most-cited researchers publishing on Image Enhancement Techniques include Kaiming He, Yoshua Bengio and Xiangyu Zhang.

  1. 1 Kaiming He Israel 562k citations
  2. 2 Yoshua Bengio Canada 476k citations
  3. 3 Xiangyu Zhang 327k citations
  4. 4 Jian Sun 281k citations
  5. 5 Wei Huang China 267k citations
  6. 6 Xiaogang Wang China 260k citations
  7. 7 Andrew Zisserman United Kingdom 255k citations
  8. 8 Li Fei-Fei United States 225k citations
  9. 9 Kai You Wang China 224k citations
  10. 10 Trevor J. Darrell United States 206k 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 Enhancement Techniques?

The most cited paper in Image Enhancement Techniques is "Perceptual Losses for Real-Time Style Transfer and Super-Resolution" (Justin M. Johnson, Alexandre Alahi, Li Fei-Fei, 2016), with 10,170 citations. Next come "Fast approximate energy minimization via graph cuts" (7,066) and "Making a “Completely Blind” Image Quality Analyzer" (6,737).

Most cited papers
#PaperReleasedCitations
1 Perceptual Losses for Real-Time Style Transfer and Super-Resolution Justin M. Johnson, Alexandre Alahi, Li Fei-Fei · Lecture notes in computer science Conference paperOpen access 2016 10,170
2 Fast approximate energy minimization via graph cuts Yuri Boykov, Olga Veksler, Ramin Zabih · IEEE Transactions on Pattern Analysis and Machine Intelligence 2001 7,066
3 Making a “Completely Blind” Image Quality Analyzer A. Mittal, Rajiv Soundararajan, Alan Conrad Bovik · IEEE Signal Processing Letters 14 Nov 2012 6,737
4 Single Image Haze Removal Using Dark Channel Prior Kaiming He, Jian Cheng Sun, Xiaoou Tang · IEEE Transactions on Pattern Analysis and Machine Intelligence 15 Sep 2010 6,281
5 High-Speed Tracking with Kernelized Correlation Filters João F. Henriques, Rui Caseiro, Pedro Martins et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence Open access Aug 2014 5,866
All 98 most cited papers, with filters and sorting

Where is Image Enhancement Techniques research done?

The largest centres of Image Enhancement Techniques research in 2022-now are Beijing (China), Shanghai (China), Xi'an (China) and Nanjing (China). Among places with at least 20 works in it, it is an unusually large share of all research in Dalian and Yantai.

Largest cities, 2022-now

  1. 1 BeijingChina 876 works
  2. 2 ShanghaiChina 374 works
  3. 3 Xi'anChina 369 works
  4. 4 NanjingChina 299 works
  5. 5 WuhanChina 293 works
  6. 6 GuangzhouChina 217 works
  7. 7 HangzhouChina 184 works
  8. 8 ChengduChina 177 works
  9. 9 DalianChina 177 works
  10. 10 HefeiChina 166 works

Where it is the local speciality

  1. DalianCN · 176.7 works7.3×
  2. YantaiCN · 36.9 works6.8×
← less than its size predicts1×more →

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

See Image Enhancement Techniques on the map

Where is the best place to study Image Enhancement Techniques?

Among universities, judged by research, Nanyang Technological University, Wuhan University and University of Science and Technology of China 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 21.2%fractional works in this field (log) →share of work in the world top 10% →Nanyang Technologica…Wuhan UniversityUniversity of Scienc…Dalian Maritime Univ…
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
1Nanyang Technological UniversitySingapore 84.631.4%5.0×33 +43.8%
2Wuhan UniversityChina 83.826.5%6.2×72 +35.3%
3University of Science and Technology of ChinaChina 79.120.9%7.1×70 +99.3%
4Dalian Maritime UniversityChina 77.819.3%23.3×70 +214.2%
5City University of Hong KongChina 77.422.6%5.2×24 +24.8%
6Northwestern Polytechnical UniversityChina 71.817.6%7.5×71 +60.0%
7Dalian University of TechnologyChina 68.219.2%6.2×48 +4.3%
8Beihang UniversityChina 67.514.7%6.3×57 +44.9%
9Chinese University of Hong KongChina 67.425.3%2.4×15 +9.7%
10Xidian UniversityChina 67.214.5%10.9×65 +13.0%

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 Enhancement Techniques, country by country

Is Image Enhancement Techniques research growing?

Output in 2022-2025 was 51% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Image Enhancement Techniques.

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 Enhancement Techniques as a percentage of every work published that year, 0.037% in 2024.

still counting0.020%0.040%0.060%Image Enhancement Techniques0.040% in 202519801990200020102020

Fractional works of Image Enhancement Techniques 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 Enhancement Techniques9.7% 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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