Skip to main content
SpringerLink
Log in

Super-resolution using lightweight detailnet network

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Image Super-Resolution is a complex method capable of converting a low-resolution image to a high-resolution image. Regarding many challenges of the Super-Resolution problem and a wide variety of its applications in image processing and man’s interpretations, it is very crucial to find an operational method. Development of deep learning methods, especially Convolutional Neural Networks has increased the power of image enhancement methods including image Super-Resolution. The goal of this research is to propose a fast speed image Super-Resolution method using Convolutional Neural Networks. The proposed DetailNet network has a small structure to prevent the problems of training very deep networks. Super-Resolution is fast by this method due to its small simple network structure. The DetailNet network is designed to add details to an input image. According to the results, DetailNet has a significant ability to produce image details. This network has a general function. Therefore, the low-resolution image size can be increased first using any method, then DetailNet can enhance the image quality and add more details to the input image. The Proposed method is applied to natural color images which achieved acceptable results on benchmark datasets.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. Multi-Layer Perceptron

  2. Structural Similarity Index

  3. Mean Square Error

  4. Generative Adversarial Network

  5. Stochastic Gradient Descent

References

  1. Bevilacqua M, Roumy A, Guillemot C, Alberi-Morel ML (2012) Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding. In: British Machine Vision Conference. BMVA Press, pp 135.1–135.10

  2. Dahl R, Norouzi M, Shlens J (2017) Pixel Recursive Super Resolution. arXiv

  3. Dong C, Loy CC, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution. In: ECCV. Springer, pp 184–199

  4. Gatys LA, Echer AS, Bethge M (2015) A neural algorithm of artistic style. arXiv Prepr arXiv150806576

  5. Gatys LA, Echer AS, Bethge M (2015) Texture synthesis using convolutional neural networks. Advances in neural information processing systems. pp 262–270

  6. Gregor K, LeCun Y (2010) Learning fast approximations of sparse coding. In: Proceedings of the 27th International Conference on International Conference on Machine Learning. Omnipress, pp 399–406

  7. Gupta P, Srivastava P, Bhardwaj S, Bhateja V (2011) A modified psnr metric based on hvs for quality assessment of color images. In: IEEE International Conference on Communication and Industrial Application (ICCIA). IEEE, pp 1–4

  8. Haris M, Shakhnarovich G, Ukita N (2018) Deep backprojection networks for super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, pp 1664–1673

  9. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: In Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, pp 770–778

  10. Huang D, Liu H (2015) A short survey of image super resolution algorithms. J Comput Sci Technol Updates 2(2):19–29

    Article  Google Scholar 

  11. Jia Y, Shelhamer E, Donahue J et al (2014) Caffe: Convolutional architecture for fast feature embedding. In: Proc. 22nd ACM Int. Conf. Multimedia. ACM pp 675–678

  12. Kim J, Lee JK, Lee KM (2016) Deeply-recursive convolutional network for image super-resolution. IEEE Conf Comput Vis Pattern Recognit IEEE, pp 1637–1645

  13. Kim J, Lee JK, Lee KM (2016) Accurate image super resolution using very deep convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 1646–1654

  14. Ledig C, Theis L, Huszar F, et al (2016) Photo-realistic single image super-resolution using a generative adversarial network. arXiv:160904802

  15. Lu X, Guo Y, Liu N, Wan L, Fang T (2018) Non-convex joint bilateral guided depth upsampling. Multimed Tools Appl Springer 77(12):15521–15544

    Article  Google Scholar 

  16. Lu X, Ma C, Ni B, Yang X, Reid I, Yang MH (2018) Deep regression tracking with shrinkage loss. In: Proceedings of the European Conference on Computer Vision (ECCV). Springer, pp 353–369

  17. Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: ICCV IEEE pp 416–423

  18. Nasrollahi K, Moeslund TB (2014) Super-resolution: a comprehensive survey. Machine vision and applications. Springer Berlin Heidelberg 25(6):1423–1468

  19. Sajjadi MSM, Scholkopf B, Hirsch M (2017) Enhancenet: Single image super-resolution through automated texture synthesis. In: IEEE International Conference on Computer Vision (ICCV). IEEE, Venice, pp 4501–4510

  20. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

  21. Sun L, Hays J (2017) Super-resolution Using Constrained Deep Texture Synthesis. CoRR, abs/170107604

  22. Tai Y, Yang J, Liu X (2017) Image super-resolution via deep recursive residual network. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE pp 3147–3155

  23. Timofte R, De Smet V, Van Gool L (2013) Anchored neighborhood regression for fast example-based super-resolution. In: Proceedings of the IEEE International Conference on Computer Vision. IEEE, pp. 1920–1927

  24. Timofte R, De Smet V, Van Gool L (2014) A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution. In: Asian Conference on Computer Vision (ACCV). Springer, pp 1–5

  25. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: From error visibility to structural similarity. In: IEEE Transactions on Image Processing. IEEE, pp 600–612

    Article  Google Scholar 

  26. Wang L, Huang Z, Gong Y, Pan C (2017) Ensemble based deep networks for image super-resolution. Pattern Recogn 68:191–198

    Article  Google Scholar 

  27. Wang Z, Liu D, Yang J, et al (2015) Deep Networks for Image Super-Resolution with Sparse Prior. In: Proceedings of the IEEE International Conference on Computer Vision. IEEE, pp 370–378

  28. Yang J, Wright J, Huang TS, Ma Y (2010) Image super resolution via sparse representation. IEEE Trans Image Process IEEE 19:2861–2873

    Article  MathSciNet  Google Scholar 

  29. Zeyde R, Elad M, Protter M (2012) On Single Image Scale-Up using Sparse-Representations. In: International Conference on Curves and Surfaces. Springer, pp 711–730

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Somayeh Barzegar & Arash Sharifi

  2. Department of Electrical and Electronic Engineering, Shahed University, Tehran, Iran

    Mohammad Manthouri

Authors
  1. Somayeh Barzegar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arash Sharifi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Manthouri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arash Sharifi.

Ethics declarations

Conflict of interest

There is no conflict of interest in this work.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Barzegar, S., Sharifi, A. & Manthouri, M. Super-resolution using lightweight detailnet network. Multimed Tools Appl 79, 1119–1136 (2020). https://doi.org/10.1007/s11042-019-08218-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08218-4

Keywords

Search

Navigation