Accelerating the Super-Resolution Convolutional Neural Network

Fully convolutional networks for semantic segmentation

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Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

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Visualizing and understanding convolutional networks

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Caffe: Convolutional Architecture for Fast Feature Embedding

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Image Super-Resolution Using Deep Convolutional Networks

Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105 · see citing works

A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

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Accurate Image Super-Resolution Using Very Deep Convolutional Networks

Kim, J., Lee, J. K., & Lee, K. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.182 · Cited by 7,870 · see citing works

Learning a Deep Convolutional Network for Image Super-Resolution

Dong, C., Loy, C. C., He, K., & Tang, X. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10593-2_13 · Cited by 5,827 · see citing works

Image Super-Resolution Via Sparse Representation

Yang, J., Wright, J. N., Huang, T. S., & Ma, Y. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2050625 · Cited by 5,241 · see citing works

Single image super-resolution from transformed self-exemplars

Huang, J., Singh, A., & Ahuja, N. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299156 · Cited by 3,603 · see citing works

On Single Image Scale-Up Using Sparse-Representations

Zeyde, R., Elad, M., & Protter, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-27413-8_47 · Cited by 3,371 · see citing works

Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding

Bevilacqua, M., Roumy, A., Guillemot, C. M., & Morel, M.-L. A. (2012) · British Machine Vision Conference (BMVC) · doi:10.5244/c.26.135 · Cited by 3,162 · see citing works

Deeply-Recursive Convolutional Network for Image Super-Resolution

Kim, J., Lee, J. K., & Lee, K. M. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.181 · Cited by 3,122 · see citing works

A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution

Timofte, R., Smet, V. D., & Gool, L. V. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16817-3_8 · Cited by 1,470 · see citing works

Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation

Denton, R., Zaremba, W., Bruna, J., LeCun, Y., & Fergus, R. (2014) · arXiv · doi:10.48550/arxiv.1404.0736 · Cited by 1,414 · see citing works

An information fidelity criterion for image quality assessment using natural scene statistics

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Anchored Neighborhood Regression for Fast Example-Based Super-Resolution

Timofte, R., De, V., & Gool, L. V. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.241 · Cited by 1,373 · see citing works

Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior

Kim, K. I., & Kwon, Y. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.25 · Cited by 993 · see citing works

Untitled work

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Deep Convolutional Neural Network for Image Deconvolution

Li, X., Ren, J., Liu, C., & Jia, J. (2014) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 836 · see citing works

Deep Networks for Image Super-Resolution with Sparse Prior

Wang, Z., Liu, D., Yang, J., Han, W., & Huang, T. S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.50 · Cited by 807 · see citing works

Single-Image Super-Resolution: A Benchmark

Yang, C. Y., Ma, C., & Yang, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10593-2_25 · Cited by 648 · see citing works

Learning to generate chairs with convolutional neural networks

Dosovitskiy, A., Springenberg, J. T., & Brox, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298761 · Cited by 640 · see citing works

Fast and accurate image upscaling with super-resolution forests

Schulter, S., Leistner, C., & Bischof, H. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299003 · Cited by 633 · see citing works

Depth Map Super-Resolution by Deep Multi-Scale Guidance

Hui, T.-W., Loy, C. C., & Tang, X. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_22 · Cited by 350 · see citing works

Fast Direct Super-Resolution by Simple Functions

Yang, C.-Y., & Yang, M. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.75 · Cited by 336 · see citing works

Deep Network Cascade for Image Super-resolution

Cui, Z., Chang, H., Shan, S., Zhong, B., & Chen, X. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_4 · Cited by 255 · see citing works

Deep Cascaded Bi-Network for Face Hallucination

Zhu, S., Liu, S., Loy, C. C., & Tang, X. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46454-1_37 · Cited by 249 · see citing works

Deeply Improved Sparse Coding for Image Super-Resolution.Preprint

Wang, Z., Liu, D., Yan, S., et al. (2015) · arXiv (Cornell University) · Cited by 39 · see citing works

Accelerating Very Deep Convolutional Networks for Classification and DetectionPreprint

Zhang, X., Zou, J., He, K., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.06798 · Cited by 30 · see citing works

Future Data and Security Engineering

Dang, T. K., Wagner, R., Neuhold, E. J., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12778-1 · Cited by 29 · see citing works

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696 · see citing works

NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study

Agustsson, E., & Timofte, R. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.150 · Cited by 4,295 · see citing works

Second-Order Attention Network for Single Image Super-Resolution

Dai, T., Cai, J., Zhang, Y., Xia, S.-T., & Zhang, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01132 · Cited by 2,010 · see citing works

Image Super-Resolution Via Iterative Refinement

Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D. J., & Norouzi, M. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3204461 · Cited by 1,829 · see citing works

Anatomically constrained neural networks (ACNNS): application to cardiac image enhancement and segmentation

Oktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, W., Caballero, J., Cook, S. A., de Marvao, A., Dawes, T., O'Regan, D. P., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2743464 · Cited by 751 · see citing works

SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network

Bai, Y., Zhang, Y., Ding, M., & Ghanem, B. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01261-8_13 · Cited by 487 · see citing works

DiffIR: Efficient Diffusion Model for Image Restoration

Xia, B., Zhang, Y., Wang, S., Wang, Y., Wu, X., Tian, Y., Yang, W., & Van Gool, L. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01204 · Cited by 412 · see citing works

Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications

Liu, M.-Y., Huang, X., Yu, J., Wang, T.-C., & Mallya, A. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3049196 · Cited by 236 · see citing works

Attribute-Guided Face Generation Using Conditional CycleGAN

Lu, Y., Tai, Y.-W., & Tang, C.-K. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01258-8_18 · Cited by 233 · see citing works

Simultaneous single-and multi-contrast super-resolution for brain MRI images based on a convolutional neural network

Zeng, K., Zheng, H., Cai, C., Yang, Y., Zhang, K., & Chen, Z. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.06.010 · Cited by 161 · see citing works

GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography

Li, W., & Hsu, C.-Y. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11070385 · Cited by 147 · see citing works

Diffusion Models, Image Super-Resolution, and Everything: A Survey

Moser, B. B., Shanbhag, A. S., Raue, F., Frolov, S., Palacio, S., & Dengel, A. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3476671 · Cited by 117 · see citing works

End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks

Wang, Y., Wang, L., Wang, H., & Li, P. (2019) · IEEE Access · doi:10.1109/access.2019.2903582 · Cited by 143 · see citing works

Technical and clinical overview of deep learning in radiology

Ueda, D., Shimazaki, A., & Miki, Y. (2019) · Japanese Journal of Radiology · doi:10.1007/s11604-018-0795-3 · Cited by 100 · see citing works

SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis

Peng, C., Lin, W.-A., Liao, H., Chellappa, R., & Zhou, S. K. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00777 · Cited by 75 · see citing works

$$\hbox {S}^2\hbox {RGAN}$$: sonar-image super-resolution based on generative adversarial network

Song, H., Wang, M., Zhang, L., Li, Y., Jiang, Z., & Yin, G. (2021) · The Visual Computer · doi:10.1007/s00371-020-01986-3 · Cited by 23 · see citing works

Back-projection-based progressive growing generative adversarial network for single image super-resolution

Ma, T., & Tian, W. (2021) · The Visual Computer · doi:10.1007/s00371-020-01843-3 · Cited by 16 · see citing works

Down/down deconvolution

Hampson, G. J., & Szumski, G. (2020) · doi:10.3997/2214-4609.2020611022 · Cited by 4 · see citing works

Increasing confidence in proteomic spectral deconvolution through mass defect

Clasen, M. A., Kurt, L. U., Santos, M. D. M., Lima, D. B., Liu, F., Gozzo, F. C., Barbosa, V. C., & Carvalho, P. C. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac638 · Cited by 19 · see citing works

Prestack nonstationary deconvolution based on variable- step sampling in the radial trace domain

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Chapter 4 Deconvolution

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Enhancing wavelet stabilitv with surface-consistent deconvolution

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