Skip to main content

Advertisement

Springer Nature Link
Log in

Fully 1 × 1 Convolutional Network for Lightweight Image Super-resolution

  • Research Article
  • Published:
Machine Intelligence Research Aims and scope Submit manuscript

Abstract

Deep convolutional neural networks, particularly large models with large kernels (3 × 3 or more), have achieved significant progress in single image super-resolution (SISR) tasks. However, the heavy computational footprint of such models prevents their deployment in real-time, resource-constrained environments. Conversely, 1 × 1 convolutions have substantial computational efficiency, but struggle with aggregating local spatial representations, which is an essential capability for SISR models. In response to this dichotomy, we propose to harmonize the merits of both 3 × 3 and 1 × 1 kernels, and exploit their great potential for lightweight SISR tasks. Specifically, we propose a simple yet effective fully 1 × 1 convolutional network, named shift-Conv-based network (SCNet). By incorporating a parameter-free spatial-shift operation, the fully 1 × 1 convolutional network is equipped with a powerful representation capability and impressive computational efficiency. Extensive experiments demonstrate that SCNets, despite their fully 1 × 1 convolutional structure, consistently match or even surpass the performance of existing lightweight SR models that employ regular convolutions. The code and pretrained models can be found at https://github.com/Aitical/SCNet.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. V. K. Ha, J. C. Ren, X. Y. Xu, S. Zhao, G. Xie, V. Masero, A. Hussain. Deep learning based single image super-resolution: A survey. International Journal of Automation and Computing, vol. 16, no. 4, pp. 413–426, 2019. DOI: https://doi.org/10.1007/s11633-019-1183-x.

    Article  Google Scholar 

  2. G. Gendy, G. H. He, N. Sabor. Lightweight image super-resolution based on deep learning: State-of-the-art and future directions. Information Fusion, vol. 94, pp. 284–310, 2023. DOI: https://doi.org/10.1016/j.inffus.2023.01.024.

    Article  Google Scholar 

  3. C. Dong, C. C. Loy, K. M. He, X. O. Tang. Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 2, pp. 295–307, 2016. DOI: https://doi.org/10.1109/TPAMI.2015.2439281.

    Article  Google Scholar 

  4. C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. H. Wang, W. Z. Shi. Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, pp. 105–114, 2017. DOI: https://doi.org/10.1109/CVPR.2017.19.

    Google Scholar 

  5. B. Lim, S. Son, H. Kim, S. Nah, K. M. Lee. Enhanced deep residual networks for single image super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, USA, pp. 1132–1140, 2017. DOI: https://doi.org/10.1109/CVPRW.2017.151.

    Google Scholar 

  6. Y. L. Zhang, Y. P. Tian, Y. Kong, B. N. Zhong, Y. Fu. Residual dense network for image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, pp. 2472–2481, 2018. DOI: https://doi.org/10.1109/CVPR.2018.00262.

    Google Scholar 

  7. M. Haris, G. Shakhnarovich, N. Ukita. Deep back-projection networks for super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, pp. 1664–1673, 2018. DOI: https://doi.org/10.1109/CVPR.2018.00179.

    Google Scholar 

  8. Z. Liu, Y. T. Lin, Y. Cao, H. Hu, Y. X. Wei, Z. Zhang, S. Lin, B. N. Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, Canada, pp. 9992–10002, 2021. DOI: https://doi.org/10.1109/ICCV48922.2021.00986.

    Google Scholar 

  9. sJ. Y. Liang, J. Z. Cao, G. L. Sun, K. Zhang, L. Van Gool, R. Timofte. SwinIR: Image restoration using swin transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, Montreal, Canada, pp. 1833–1844, 2021. DOI: https://doi.org/10.1109/ICCVW54120.2021.00210.

    Google Scholar 

  10. C. Dong, C. C. Loy, X. O. Tang. Accelerating the super-resolution convolutional neural network. In Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands, pp. 391–407, 2016. DOI: https://doi.org/10.1007/978-3-319-46475-6_25.

    Google Scholar 

  11. Ahn, B. Kang, K. A. Sohn. Fast, accurate, and lightweight super-resolution with cascading residual network. In Proceedings of the 15th European Conference on Computer Vision, Munich, Germany, pp. 256–272, 2018. DOI: https://doi.org/10.1007/978-3-030-01249-6_16.

    Google Scholar 

  12. L. G. Wang, X. Y. Dong, Y. Q. Wang, X. Y. Ying, Z. P. Lin, W. An, Y. L. Guo. Exploring sparsity in image super-resolution for efficient inference. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, pp. 4915–4924, 2021. DOI: https://doi.org/10.1109/CVPR46437.2021.00488.

    Google Scholar 

  13. X. D. Zhang, H. Zeng, L. Zhang. Edge-oriented convolution block for real-time super resolution on mobile devices. In Proceedings of the 29th ACM International Conference on Multimedia, pp. 4034–4043, 2021. DOI: https://doi.org/10.1145/3474085.3475291.3.

    Chapter  Google Scholar 

  14. G. W. Gao, W. J. Li, J. C. Li, F. Wu, H. M. Lu, Y. Yu. Feature distillation interaction weighting network for lightweight image super-resolution. In Proceedings of the 36th AAAI Conference on Artificial Intelligence, pp. 661–669, 2022. DOI: https://doi.org/10.1609/aaai.v36i1.19946.

    Google Scholar 

  15. J. M. Li, T. Dai, M. Y. Zhu, B. Chen, Z. Wang, S. T. Xia. FSR: A general frequency-oriented framework to accelerate image super-resolution networks. In Proceedings of the 37th AAAI Conference on Artificial Intelligence, Washington DC, USA, pp. 1343–1350, 2023. DOI: https://doi.org/10.1609/aaai.v37i1.25218.

    Google Scholar 

  16. Z. Liu, H. Z. Mao, C. Y. Wu, C. Feichtenhofer, T. Darrell, S. N. Xie. A ConvNet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, pp. 11966–11976, 2022. DOI: https://doi.org/10.1109/CVPR52688.2022.01167.

    Google Scholar 

  17. X. H. Ding, X. Y. Zhang, Y. G. Zhou, J. G. Han, G. G. Ding, J. Sun. Scaling up your kernels to 31×31: Revisiting large kernel design in CNNs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, pp. 11953–11965, 2022. DOI: https://doi.org/10.1109/CVPR52688.2022.01166.

    Google Scholar 

  18. J. Lin, C. Gan, S. Han. TSM: Temporal shift module for efficient video understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea, pp. 7082–7092, 2019. DOI: https://doi.org/10.1109/ICCV.2019.00718.

    Google Scholar 

  19. W. J. Chen, D. Xie, Y. Zhang, S. L. Pu. All you need is a few shifts: Designing efficient convolutional neural networks for image classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, pp. 7234–7243, 2019. DOI: https://doi.org/10.1109/CVPR.2019.00741.

    Google Scholar 

  20. J. F. Dai, H. Z. Qi, Y. W. Xiong, Y. Li, G. D. Zhang, H. Hu, Y. C. Wei. Deformable convolutional networks. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, pp. 764–773, 2017. DOI: https://doi.org/10.1109/ICCV.2017.89.

    Google Scholar 

  21. L. L. Jing, Y. L. Tian. Self-supervised visual feature learning with deep neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 11, pp. 4037–4058, 2021. DOI: https://doi.org/10.1109/TPAMI.2020.2992393.

    Article  Google Scholar 

  22. J. C. Li, Z. H. Pei, T. Y. Zeng. From beginner to master: A survey for deep learning-based single-image super-resolution, [Online], Available: https://arxiv.org/abs/2109.14335, 2021.

    Google Scholar 

  23. J. Kim, J. K. Lee, K. M. Lee. Accurate image super-resolution using very deep convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 1646–1654, 2016. DOI: https://doi.org/10.1109/CVPR.2016.182.

    Google Scholar 

  24. Y. Tai, J. Yang, X. M. Liu. Image super-resolution via deep recursive residual network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, pp. 2790–2798, 2017. DOI: https://doi.org/10.1109/CVPR.2017.298.

    Google Scholar 

  25. J. Hu, L. Shen, G. Sun. Squeeze-and-excitation networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, pp. 7132–7141, 2018. DOI: https://doi.org/10.1109/CVPR.2018.00745.

    Google Scholar 

  26. Y. L. Zhang, K. P. Li, K. Li, L. C. Wang, B. N. Zhong, Y. Fu. Image super-resolution using very deep residual channel attention networks. In Proceedings of the 15th European Conference on Computer Vision, Munich, Germany, pp. 294–310, 2018. DOI: https://doi.org/10.1007/978-3-030-01234-2_18.

    Google Scholar 

  27. T. Dai, J. R. Cai, Y. B. Zhang, S. T. Xia, L. Zhang. Second-order attention network for single image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, pp. 11057–11066, 2019. DOI: https://doi.org/10.1109/CVPR.2019.01132.

    Google Scholar 

  28. B. Niu, W. L. Wen, W. Q. Ren, X. D. Zhang, L. P. Yang, S. Z. Wang, K. H. Zhang, X. C. Cao, H. F. Shen. Single image super-resolution via a holistic attention network. In Proceedings of the 16th European Conference on Computer Vision, Glasgow, UK, pp. 191–207, 2020. DOI: https://doi.org/10.1007/978-3-030-58610-2_12.

    Google Scholar 

  29. A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. H. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In Proceedings of the 9th International Conference on Learning Representations, 2021.

    Google Scholar 

  30. H. T. Chen, Y. H. Wang, T. Y. Guo, C. Xu, Y. P. Deng, Z. H. Liu, S. W. Ma, C. J. Xu, C. Xu, W. Gao. Pre-trained image processing transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, pp. 12294–12305, 2021. DOI: https://doi.org/10.1109/CVPR46437.2021.01212.

    Google Scholar 

  31. K. Zhang, Y. W. Li, J. Y. Liang, J. Z. Cao, Y. L. Zhang, H. Tang, D. P. Fan, R. Timofte, L. Van Gool. Practical blind image denoising via swin-conv-UNet and data synthesis. Machine Intelligence Research, vol. 20, no. 6, pp. 822–836, 2023. DOI: https://doi.org/10.1007/s11633-023-1466-0.

    Article  Google Scholar 

  32. H. Wang, Y. L. Zhang, C. Qin, L. Van Gool, Y. Fu. Global aligned structured sparsity learning for efficient image super-resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 9, pp. 10974–10989, 2023. DOI: https://doi.org/10.1109/TPAMI.2023.3268675.

    Article  Google Scholar 

  33. G. Wu, J. J. Jiang, X. M. Liu. A practical contrastive learning framework for single-image super-resolution. IEEE Transactions on Neural Networks and Learning Systems, to be published. DOI: https://doi.org/10.1109/TNNLS.2023.3290038.

  34. G. Wu, J. J. Jiang, K. Jiang, X. M. Liu. Learning from history: Task-agnostic model contrastive learning for image restoration. In Proceedings of the 38th AAAI Conference on Artificial Intelligence, Vancouver, Canada, pp. 5976–5984, 2024.

    Google Scholar 

  35. Y. M. Zhang, H. T. Chen, X. H. Chen, Y. P. Deng, C. J. Xu, Y. H. Wang. Data-free knowledge distillation for image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, pp. 7848–7857, 2021. DOI: https://doi.org/10.1109/CV-PR46437.2021.00776.

    Google Scholar 

  36. H. Zhao, O. Gallo, I. Frosio, J. Kautz. Loss functions for image restoration with neural networks. IEEE Transactions on Computational Imaging, vol. 3, no. 1, pp. 47–57, 2017. DOI: https://doi.org/10.1109/TCI.2016.2644865.

    Article  Google Scholar 

  37. Z. Hui, X. M. Wang, X. B. Gao. Fast and accurate single image super-resolution via information distillation network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, pp. 723–731, 2018. DOI: https://doi.org/10.1109/CVPR.2018.00082.

    Google Scholar 

  38. Z. Hui, X. B. Gao, Y. C. Yang, X. M. Wang. Lightweight image super-resolution with information multi-distillation network. In Proceedings of the 27th ACM International Conference on Multimedia, Nice, France, pp. 2024–2032, 2019. DOI: https://doi.org/10.1145/3343031.3351084.

    Chapter  Google Scholar 

  39. W. B. Li, K. Zhou, L. Qi, N. J. Jiang, J. B. Lu, J. Y. Jia. LAPAR: Linearly-assembled pixel-adaptive regression network for single image super-resolution and beyond. In Proceedings of the 34th International Conference on Neural Information Processing Systems, Article number 1708, 2020.

    Google Scholar 

  40. B. Li, B. Wang, J. B. Liu, Z. Q. Qi, Y. Shi. s-LWSR: Super lightweight super-resolution network. IEEE Transactions on Image Processing, vol. 29, pp. 8368–8380, 2020. DOI: https://doi.org/10.1109/TIP.2020.3014953.

    Article  Google Scholar 

  41. L. Sun, J. S. Pan, J. H. Tang. ShuffleMixer: An efficient ConvNet for image super-resolution. In Proceedings of the 36th International Conference on Neural Information Processing Systems, New Orleans, USA, 2022.

    Google Scholar 

  42. B. C. Wu, A. Wan, X. Y. Yue, P. H. Jin, S. C. Zhao, N. Golmant, A. Gholaminejad, J. Gonzalez, K. Keutzer. Shift: A zero flop, ZERO parameter alternative to spatial convolutions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, pp. 9127–9135, 2018. DOI: https://doi.org/10.1109/CV-PR.2018.00951.

    Google Scholar 

  43. Y. Jeon, J. Kim. Constructing fast network through deconstruction of convolution. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montreal, Canada, pp. 5955–5965, 2018.

    Google Scholar 

  44. X. D. Zhang, H. Zeng, S. Guo, L. Zhang. Efficient long-range attention network for image super-resolution. In Proceedings of the 17th European Conference on Computer Vision, Tel Aviv, Israel, pp. 649–667, 2022. DOI: https://doi.org/10.1007/978-3-031-19790-1_39.

    Google Scholar 

  45. D. P. Kingma, J. Ba. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations, San Diego, USA, 2015.

    Google Scholar 

  46. E. Agustsson, R. Timofte. NTIRE 2017 challenge on single image super-resolution: Dataset and study. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, USA, pp. 1122–1131, 2017. DOI: https://doi.org/10.1109/CVPRW.2017.150.

    Google Scholar 

  47. M. Bevilacqua, A. Roumy, C. Guillemot, M. L. Alberi-Morel. Low-complexity single-image super-resolution based on non-negative neighbor embedding. In Proceedings of the British Machine Vision Conference, Surrey, UK, pp. 1–10, 2012.

    Google Scholar 

  48. R. Zeyde, M. Elad, M. Protter. On single image scale-up using sparse-representations. In Proceedings of the 7th International Conference on Curves and Surfaces, Avignon, France, pp. 711–730, 2010.DOI: https://doi.org/10.1007/978-3-642-27413-8_47.

    Google Scholar 

  49. D. Martin, C. Fowlkes, D. Tal, J. Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings of the 8th IEEE International Conference on Computer Vision, Vancouver, Canada, pp. 416–423, 2001. DOI: https://doi.org/10.1109/ICCV.2001.937655.

    Google Scholar 

  50. J. B. Huang, A. Singh, N. Ahuja. Single image super-resolution from transformed self-exemplars. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, pp. 5197–5206, 2015. DOI: https://doi.org/10.1109/CVPR.2015.7299156.

    Google Scholar 

  51. Y. Matsui, K. Ito, Y. Aramaki, A. Fujimoto, T. Ogawa, T. Yamasaki, K. Aizawa. Sketch-based manga retrieval using manga109 dataset. Multimedia Tools and Applications, vol. 76, no. 20, pp. 21811–21838, 2017. DOI: https://doi.org/10.1007/s11042-016-4020-z.

    Article  Google Scholar 

  52. W. S. Lai, J. B. Huang, N. Ahuja, M. H. Yang. Deep Laplacian pyramid networks for fast and accurate super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, pp. 5835–5843, 2017. DOI: https://doi.org/10.1109/CVPR.2017.618.

    Google Scholar 

  53. G. W. Gao, Z. X. Wang, J. C. Li, W. J. Li, Y. Y. Yu, T. Zeng. Lightweight bimodal network for single-image super-resolution via symmetric CNN and recursive transformer. In Proceedings of the 31st International Joint Conference on Artificial Intelligence, Vienna, Austria, pp. 913–919, 2022.

    Google Scholar 

  54. J. Kim, J. K. Lee, K. M. Lee. Deeply-recursive convolutional network for image super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 1637–1645, 2016. DOI: https://doi.org/10.1109/CVPR.2016.181.

    Google Scholar 

  55. Z. Li, J. L. Yang, Z. Liu, X. M. Yang, G. Jeon, W. Wu. Feedback network for image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, pp. 3862–3871, 2019. DOI: https://doi.org/10.1109/CVPR.2019.00399.

    Google Scholar 

  56. J. J. Gu, C. Dong. Interpreting super-resolution networks with local attribution maps. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, pp. 9195–9204, 2021. DOI: https://doi.org/10.1109/CVPR46437.2021.00908.

    Google Scholar 

  57. C. F. Wang, Z. Li, J. Shi. Lightweight image super-resolution with adaptive weighted learning network, [Online], Available: https://arxiv.org/abs/1904.02358, 2019.

    Google Scholar 

Download references

Acknowledgements

The research was supported by the National Natural Science Foundation of China, China (Nos. U23B2009 and 92270116), and was partially supported by the Fundamental Research Funds for the Central Universities, China.

Author information

Authors and Affiliations

  1. Faculty of Computing, Harbin Institute of Technology, Harbin, 150001, China

    Gang Wu, Junjun Jiang, Kui Jiang & Xianming Liu

Authors
  1. Gang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junjun Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kui Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xianming Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Junjun Jiang.

Ethics declarations

The authors declared that they have no conflicts of interest to this work.

Additional information

Gang Wu received the B. Eng. degree in computer science from the School of Computer Science and Technology, Soochow University, China in 2020. He is currently a Ph. D. degree candidate in Faculty of Computing, Harbin Institute of Technology, China.

His research interests include image restoration, representation learning and self-supervised learning.

Junjun Jiang received the B. Sc. degree in mathematics from the Huaqiao University, China in 2009, and the Ph. D. degree in computer science from Wuhan University, China in 2014. From 2015 to 2018, he was an associate professor with the School of Computer Science, China University of Geosciences, China. From 2016 to 2018, he was a Project Researcher with the National Institute of Informatics (NII), Japan. He is currently a professor with the School of Computer Science and Technology, Harbin Institute of Technology, China. He won the Best Student Paper Runner-up Award at MMM 2017, the Finalist of the World’s FIRST 10 K Best Paper Award at ICME 2017, and the Best Paper Award at IFTC 2018. He received the 2016 China Computer Federation (CCF) Outstanding Doctoral Dissertation Award and 2015 ACM Wuhan Doctoral Dissertation Award.

His research interests include image processing and computer vision.

Kui Jiang received the M. Eng. and Ph. D. degrees in computer science from the School of Computer Science, Wuhan University, China in 2019 and 2022, respectively. Before July 2023, he was a research scientist with the Cloud BU, Huawei, China. He is currently an associate professor with the School of Computer Science and Technology, Harbin Institute of Technology, China. He received the 2022 ACM Wuhan Doctoral Dissertation Award, China

His research interests include image/video processing and computer vision.

Xianming Liu received the B. Sc., M. Sc., and Ph. D. degrees in computer science from the Harbin Institute of Technology (HIT), China in 2006, 2008 and 2012, respectively. In 2011, he spent half a year at the Department of Electrical and Computer Engineering, McMaster University, Canada, as a visiting student, where he was a post-doctoral fellow from 2012 to 2013. He was a project researcher with the National Institute of Informatics (NII), Japan from 2014 to 2017. He is currently a professor with the School of Computer Science and Technology, HIT, China. He was a receipt of the IEEE ICME 2016 Best Student Paper Award.

His research interests include trustworthy AI, computational imaging, biomedical signal compression and 3D signal processing and analysis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, G., Jiang, J., Jiang, K. et al. Fully 1 × 1 Convolutional Network for Lightweight Image Super-resolution. Mach. Intell. Res. 21, 1062–1076 (2024). https://doi.org/10.1007/s11633-024-1501-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11633-024-1501-9

Keywords

Search

Navigation