Advertisement

A fast single-image super-resolution method implemented with CUDA

  • Yuan Yuan
  • Xiaomin Yang
  • Wei Wu
  • Hu Li
  • Yiguang Liu
  • Kai Liu
Special Issue Paper
  • 108 Downloads

Abstract

Image super-resolution (SR) plays an important role in many areas as it promises to generate high-resolution (HR) images without upgrading image sensors. Many existing SR methods require a large external training set, which would consume a lot of memory. In addition, these methods are usually time-consuming when training model. Moreover, these methods need to retrain model once the magnification factor changes. To overcome these problems, we propose a method, which does not need an external training set by using self-similarity. Firstly, we rotate original low-resolution (LR) image with different angles to expand the training set. Second, multi-scale Difference of Gaussian filters are exploited to obtain multi-view feature maps. Multi-view feature maps could provide an accurate representation of images. Then, feature maps are divided into patches in parallel to build an internal training set. Finally, nonlocal means is applied to each LR patch from original LR image to infer HR patches. In order to accelerate the proposed method by exploiting the computation power of GPU, we implement the proposed method with compute unified device architecture (CUDA). Experimental results validate that the proposed method performs best among the compared methods in both terms of visual perception and objective quantitation. Moreover, the proposed method gets a remarkable speedup after implemented with CUDA.

Keywords

Super-resolution Self-similarity GPU CUDA 

Notes

Acknowledgements

The research in our paper is sponsored by National Natural Science Foundation of China (Nos. 61701327, 61711540303, 61473198), also is supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) Fund, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET) Fund.

References

  1. 1.
    Park, S.C., Park, M.K., Kang, M.G.: Super-resolution image reconstruction: a technical overview. IEEE Signal Process. Mag. 20(3), 21–36 (2003)CrossRefGoogle Scholar
  2. 2.
    Li, X., Hu, Y., Gao, X., Tao, D., Ning, B.: A multi-frame image super-resolution method. Sig. Process. 90(2), 405–414 (2010)CrossRefzbMATHGoogle Scholar
  3. 3.
    Aguena, M.L., Mascarenhas, N.D.: Multispectral image data fusion using POCS and super-resolution. Comput. Vis. Image Underst. 102(2), 178–187 (2006)CrossRefGoogle Scholar
  4. 4.
    Qin, F.Q., He, X.H., Chen, W.L., Yang, X.M., Wu, W.: Video superresolution reconstruction based on subpixel registration and iterative back projection. J. Electron. Imaging 18(1), 013007 (2009)CrossRefGoogle Scholar
  5. 5.
    Vrigkas, M., Nikou, C., Kondi, L.P.: Accurate image registration for MAP image super-resolution. Sig. Process. Image Commun. 28(5), 494–508 (2013)CrossRefGoogle Scholar
  6. 6.
    Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 349–356. IEEE (2009)Google Scholar
  7. 7.
    Kim, K.I., Kwon, Y.: Single-image super-resolution using sparse regression and natural image prior. IEEE Trans. Pattern Anal. Mach. Intell. 32(6), 1127–1133 (2010)CrossRefGoogle Scholar
  8. 8.
    Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: International Conference on Curves and Surfaces, pp. 711–730. Springer, Berlin (2010)Google Scholar
  9. 9.
    Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Yang, X., Wu, W., Liu, K., Chen, W., Zhang, P., Zhou, Z.: Multi-sensor image super-resolution with fuzzy cluster by using multi-scale and multi-view sparse coding for infrared image. Multimed. Tools Appl. 76(2), 1–32 (2017)CrossRefGoogle Scholar
  11. 11.
    Wu, W., Yang, X., Liu, K., Liu, Y., Yan, B.: A new framework for remote sensing image super-resolution: sparse representation-based method by processing dictionaries with multi-type features. J. Syst. Architect. 64, 63–75 (2016)CrossRefGoogle Scholar
  12. 12.
    Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: European Conference on Computer Vision, pp. 184–199. Springer, Cham (2014, September)Google Scholar
  13. 13.
    Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197–5206 (2015)Google Scholar
  14. 14.
    Zhou, Z., Wang, Y., Wu, Q.J., Yang, C.N., Sun, X.: Effective and efficient global context verification for image copy detection. IEEE Trans. Inf. Forensics Secur. 12(1), 48–63 (2017)CrossRefGoogle Scholar
  15. 15.
    Li, J., Li, X., Yang, B., Sun, X.: Segmentation-based image copy-move forgery detection scheme. IEEE Trans. Inf. Forensics Secur. 10(3), 507–518 (2015)CrossRefGoogle Scholar
  16. 16.
    Pan, Z., Zhang, Y., Kwong, S.: Efficient motion and disparity estimation optimization for low complexity multiview video coding. IEEE Trans. Broadcast. 61(2), 166–176 (2015)CrossRefGoogle Scholar
  17. 17.
    Rong, H., Ma, T., Tang, M.: A novel subgraph K+-isomorphism method is social network based on graph similarity detection. Soft. Comput. (2017).  https://doi.org/10.1007/s00500-017-2513-y Google Scholar
  18. 18.
    Zhou, Z., Wu, Q.J., Huang, F., Sun, X.: Fast and accurate near-duplicate image elimination for visual sensor networks. Int. J. Distrib. Sens. Netw. 13(2), 1550147717694172 (2017)CrossRefGoogle Scholar
  19. 19.
    Zhou, Z., Yang, C.N., Chen, B., Sun, X., Liu, Q., Qm, J.: Effective and efficient image copy detection with resistance to arbitrary rotation. IEICE Trans. Inf. Syst. 99(6), 1531–1540 (2016)CrossRefGoogle Scholar
  20. 20.
    Yang, C.Y., Huang, J.B., Yang, M.H.: Exploiting self-similarities for single frame super-resolution. In: Asian Conference on Computer Vision, pp. 497–510. Springer, Berlin (2010)Google Scholar
  21. 21.
    Suetake, N., Sakano, M., Uchino, E.: Image super-resolution based on local self-similarity. Opt. Rev. 15(1), 26–30 (2008)CrossRefGoogle Scholar
  22. 22.
    Wu, W., Zheng, C.: Single image super-resolution using self-similarity and generalized nonlocal mean. In: TENCON 2013-2013 IEEE Region 10 Conference, pp 1–4. IEEE (2013)Google Scholar
  23. 23.
    Sun, K.Z., Li, J.D., Xu, S.Y.: Gpu-Accelerated non-local means super-resolution reconstruction. In: Proceedings of the 3rd International Conference on Multimedia Technology, ICMT, pp. 1242–1249 (2013)Google Scholar
  24. 24.
    Wang, Y.K., Huang, W.B.: A CUDA-enabled parallel algorithm for accelerating retinex. J. Real Time Image Proc. 9(3), 407–425 (2014)CrossRefGoogle Scholar
  25. 25.
    Document Page of Nvidia. http://docs.nvidia.com/cuda/cuda-c-best-practices-guide. Accessed 18 Dec 2017
  26. 26.
    Home Page of Nvidia. https://developer.nvidia.com/. Accessed 18 Dec 2017
  27. 27.
    Garcia, V., Debreuve, E., Nielsen, F., Barlaud, M.: K-nearest neighbor search: fast GPU-based implementations and application to high-dimensional feature matching. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 3757–3760. IEEE (2010)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yuan Yuan
    • 1
  • Xiaomin Yang
    • 1
  • Wei Wu
    • 1
  • Hu Li
    • 1
  • Yiguang Liu
    • 2
  • Kai Liu
    • 3
  1. 1.College of Electronics and Information EngineeringSichuan UniversityChengduChina
  2. 2.College of Computer ScienceSichuan UniversityChengduChina
  3. 3.College of Electrical and Engineering InformationSichuan UniversityChengduChina

Personalised recommendations