Most cited papers in Image Processing Techniques and Applications
What are the most cited papers in Image Processing Techniques and Applications?
The most cited paper in Image Processing Techniques and Applications is "U-Net: Convolutional Networks for Biomedical Image Segmentation" (Olaf Ronneberger, Philipp F. Fischer, Thomas Brox, 2015), with 92,751 citations. Next come "Very Deep Convolutional Networks for Large-Scale Image Recognition" (75,434) and "NIH Image to ImageJ: 25 years of image analysis" (67,169).
Showing 50 of 98 papers. Every paper with more than 1,282 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger, Philipp F. Fischer, Thomas Brox · Lecture notes in computer science | 2015 | 92,751 |
| 2 | Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan, Andrew Zisserman · arXiv (Cornell University) | 4 Sep 2014 | 75,434 |
| 3 | NIH Image to ImageJ: 25 years of image analysis Caroline A Schneider, Wayne S. Rasband, Kevin W. Eliceiri · Nature Methods | 28 Jun 2012 | 67,169 |
| 4 | The Pascal Visual Object Classes (VOC) Challenge Mark Everingham, Luc Van Gool, Christopher K. I. Williams et al. · International Journal of Computer Vision | 8 Sep 2009 | 20,137 |
| 5 | A flexible new technique for camera calibration Zheng Zhang · IEEE Transactions on Pattern Analysis and Machine Intelligence | 2000 | 14,989 |
| 6 | A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper) Khanam, Zeba, Achari, Vejey Pradeep Suresh, Boukhennoufa, Issam et al. · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) | 2024 | 14,399 |
| 7 | The Unreasonable Effectiveness of Deep Features as a Perceptual Metric Richard Zhang, Phillip Isola, Alexei A. Efros et al. | Jun 2018 | 13,833 |
| 8 | Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Christian Ledig, Lucas Theis, Ferenc Huszár et al. | Jul 2017 | 12,615 |
| 9 | Image Super-Resolution Using Deep Convolutional Networks Chao Dong, Chen Change Loy, Kaiming He et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 2 Jun 2015 | 10,149 |
| 10 | Accurate Image Super-Resolution Using Very Deep Convolutional Networks Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee | Jun 2016 | 7,784 |
| 11 | Determining Optical Flow Berthold K. P. Horn, Brian G. Schunck · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE | 12 Nov 1981 | 7,486 |
| 12 | Fundamentals of digital image processing Anil Kumar Jain | 3 Oct 1988 | 7,013 |
| 13 | A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses R. Tsai · IEEE Journal on Robotics and Automation | Aug 1987 | 5,863 |
| 14 | CTFFIND4: Fast and accurate defocus estimation from electron micrographs Alexis L. Rohou, Nikolaus Grigorieff · Journal of Structural Biology | 13 Aug 2015 | 5,758 |
| 15 | Learning a Deep Convolutional Network for Image Super-Resolution Chao Dong, Chen Change Loy, Kaiming He et al. · Lecture notes in computer science | 2014 | 5,746 |
| 16 | Image Super-Resolution Using Very Deep Residual Channel Attention Networks Yulun Zhang, Kunpeng Li, Kai Li et al. · Lecture notes in computer science | 2018 | 5,660 |
| 17 | CellProfiler: image analysis software for identifying and quantifying cell phenotypes Anne E. Carpenter, Thouis R. Jones, Michael R. Lamprecht et al. · Genome biology | 31 Oct 2006 | 5,571 |
| 18 | Dimensionality Reduction by Learning an Invariant Mapping Raia T. Hadsell, Sumit Chopra, Yann LeCun | 10 Jul 2006 | 5,318 |
| 19 | Cellpose: a generalist algorithm for cellular segmentation Carsen Stringer, Tim Wang, Michalis Michaelos et al. · Nature Methods | 14 Dec 2020 | 4,662 |
| 20 | SwinIR: Image Restoration Using Swin Transformer Jingyun Liang, Jiezhang Cao, Guolei Sun et al. | Oct 2021 | 4,644 |
| 21 | Bayesian-Based Iterative Method of Image Restoration* William Hadley Richardson · Journal of the Optical Society of America | 1972 | 4,234 |
| 22 | Residual Dense Network for Image Super-Resolution Yulun Zhang, Yapeng Tian, Yu Xiang George Kong et al. | Jun 2018 | 4,180 |
| 23 | Restormer: Efficient Transformer for High-Resolution Image Restoration Syed Waqas Zamir, Aditya Arora, Salman Khan et al. · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) | Jun 2022 | 4,022 |
| 24 | Computer Vision: A Modern Approach David Alexander Forsyth, Jean Ponce | 2002 | 3,740 |
| 25 | Robust wide-baseline stereo from maximally stable extremal regions Jiřı́ Matas, Ondřej Chum, M. Urban et al. · Image and Vision Computing | 28 Jun 2004 | 3,705 |
| 26 | Accelerating the Super-Resolution Convolutional Neural Network Chao Dong, Chen Change Loy, Xiaoou Tang · Lecture notes in computer science | 2016 | 3,666 |
| 27 | Single image super-resolution from transformed self-exemplars Jia‐Bin Huang, Abhishek Singh, Narendra Ahuja | Jun 2015 | 3,607 |
| 28 | Unsupervised Monocular Depth Estimation with Left-Right Consistency Clément Godard, Oisin Mac Aodha, Gabriel Brostow | Jul 2017 | 3,330 |
| 29 | Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review Bing Pan, Qian Kemao, Huimin Xie et al. · Measurement Science and Technology | 27 Apr 2009 | 3,156 |
| 30 | Deeply-Recursive Convolutional Network for Image Super-Resolution Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee | Jun 2016 | 3,132 |
| 31 | Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution Wei‐Sheng Lai, Jia‐Bin Huang, Narendra Ahuja et al. | Jul 2017 | 3,096 |
| 32 | Locality-constrained Linear Coding for image classification Jinjun Wang, Jianchao Yang, Kai Yu et al. | Jun 2010 | 3,079 |
| 33 | Two-Frame Motion Estimation Based on Polynomial Expansion Gunnar Farnebäck · Lecture notes in computer science | 2003 | 3,038 |
| 34 | Random Erasing Data Augmentation Zhun Zhong, Liang Zheng, Guoliang Kang et al. · Proceedings of the AAAI Conference on Artificial Intelligence | 3 Apr 2020 | 2,959 |
| 35 | A Comparison of Affine Region Detectors Krystian Mikolajczyk, Tinne Tuytelaars, C. Schmid et al. · International Journal of Computer Vision | 14 Oct 2005 | 2,933 |
| 36 | The ImageJ ecosystem: An open platform for biomedical image analysis Johannes Schindelin, Curtis Rueden, Mark Hiner et al. · Molecular Reproduction and Development | Jul 2015 | 2,786 |
| 37 | Flexible camera calibration by viewing a plane from unknown orientations Zhengyou Zhang | 1999 | 2,618 |
| 38 | Feature Detection with Automatic Scale Selection Tony Lindeberg · International Journal of Computer Vision | Nov 1998 | 2,540 |
| 39 | Visual servo control. I. Basic approaches François Chaumette, Seth Hutchinson · IEEE Robotics & Automation Magazine | Dec 2006 | 2,521 |
| 40 | Image Super-Resolution via Deep Recursive Residual Network Ying Yu Tai, Jian Jun Yang, Xiaoming Liu | Jul 2017 | 2,514 |
| 41 | Computer vision and pattern recognition Nanning Zheng, George D. Loizou, Xiaoyi Jiang et al. · International Journal of Computer Mathematics | 30 Aug 2007 | 2,469 |
| 42 | Precise Nanometer Localization Analysis for Individual Fluorescent Probes Russell E. Thompson, Daniel R. Larson, Watt W. Webb · Biophysical Journal | May 2002 | 2,369 |
| 43 | Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring Seungjun Nah, Tae Hyun Kim, Kyoung Mu Lee | Jul 2017 | 2,367 |
| 44 | Pixelwise View Selection for Unstructured Multi-View Stereo Johannes L. Schönberger, Enliang Zheng, Jan-Michael Frahm et al. · Lecture notes in computer science | 2016 | 2,365 |
| 45 | Globally and locally consistent image completion Satoshi Iizuka, Edgar Simo‐Serra, Hiroshi Ishikawa · ACM Transactions on Graphics | 20 Jul 2017 | 2,350 |
| 46 | Rapid Molecular Detection of Tuberculosis and Rifampin Resistance Catharina C. Boehme, Pamela Nabeta, Doris Hillemann et al. · New England Journal of Medicine | 8 Sep 2010 | 2,336 |
| 47 | Ncorr: Open-Source 2D Digital Image Correlation Matlab Software Justin A. Blaber, Benjamin S. Adair, Antonia Maki Antoniou · Experimental Mechanics | 31 Mar 2015 | 2,298 |
| 48 | Depth Map Prediction from a Single Image using a Multi-Scale Deep Network David Eigen, Christian Puhrsch, Rob Fergus · arXiv (Cornell University) | 9 Jun 2014 | 2,263 |
| 49 | Uformer: A General U-Shaped Transformer for Image Restoration Zhendong Wang, Xiaodong Cun, Jianmin Bao et al. · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) | Jun 2022 | 2,225 |
| 50 | Minimum error thresholding Josef Kittler, John Illingworth · Pattern Recognition | 1986 | 2,178 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.