Soft Computing

, Volume 22, Issue 5, pp 1385–1398 | Cite as

Multiple dictionary pairs learning and sparse representation-based infrared image super-resolution with improved fuzzy clustering

  • Xiaomin Yang
  • Wei Wu
  • Kai Liu
  • Weilong Chen
  • Zhili Zhou
Focus

Abstract

Exploring sparse representation to enhance the resolution of infrared image has attracted much attention in the last decade. However, conventional sparse representation-based super-resolution aim at learning a universal and efficient dictionary pair for image representation. However, considering that a large number of different structures exist in an image, it is insufficient and unreasonable to present various image structures with only one universal dictionary pair. In this paper, we propose an improved fuzzy clustering and weighted scheme reconstruction framework to solve this problem. Firstly, the training patches are divided into multiple clusters by joint learning multiple dictionary pairs with improved fuzzy clustering method. The goal of joint learning is to learn the multiple dictionary pairs which could collectively represent all the training patches with smallest reconstruction error. So that the learned dictionary pairs are more precise and mutually complementary. Then, high-resolution (HR) patches are estimated according to several most accurate dictionary pairs. Finally, these estimated HR patches are integrated together to generate a final HR patch by a weighted scheme. Numerous experiments demonstrate that this framework outperforms some state-of-art super-resolution methods in both quantitatively and perceptually.

Keywords

Multi-sensor Super-resolution Sparse representation Infrared image Dictionary learning Multiview representation Fuzzy clustering theory 

Notes

Acknowledgements

The research is sponsored by the National Natural Science Foundation of China (Nos. 61701327, 61711540303 and 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.

Compliance with ethical standards

Conflict of interest

All the authors declare that they have no conflict of interest.

References

  1. Chang H, Yeung DY, Xiong Y (2004) Super-resolution through neighbor embedding. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004. CVPR 2004, vol 1, pp I-275–I-282Google Scholar
  2. Chen X, Jeon G, Jeong J (2014) Voting-based directional interpolation method and its application to still color image demosaicking. IEEE Trans Circuits Syst Video Technol 24(2):255–262CrossRefGoogle Scholar
  3. Chen X, He L, Jeon G, Jeong J (2015) Multidirectional weighted interpolation and refinement method for Bayer pattern CFA demosaicking. IEEE Trans Circuits Syst Video Technol 25(8):1271–1282CrossRefGoogle Scholar
  4. Farsiu S, Dirk Robinson M, Elad M, Milanfar P (2004) Fast and robust multiframe super resolution. IEEE Trans Image Process A Publ IEEE Signal Process Soc 13(10):1327–1344CrossRefGoogle Scholar
  5. Freeman WT, Jones TR, Pasztor EC (2002) Example-based super-resolution. IEEE Comput Graph Appl 22(2):56–65CrossRefGoogle Scholar
  6. Gu B, Sun X, Sheng VS (2016) Structural minimax probability machine. IEEE Trans Neural Netw Learn Syst PP(99):1–11Google Scholar
  7. Jeon G, Anisetti M, Kim D, Bellandi V, Damiani E, Jeong J (2009) Fuzzy rough sets hybrid scheme for motion and scene complexity adaptive deinterlacing. Image Vis Comput 27(4):425–436CrossRefGoogle Scholar
  8. Jeon G, Anisetti M, Kang SH (2013) A rank-ordered marginal filter for deinterlacing. Sensors 13(3):3056–65CrossRefGoogle Scholar
  9. Kimmel R (1999) Demosaicing: image reconstruction from color CCD samples. IEEE Trans Image Process A Publ IEEE Signal Process Soc 8(9):1221–1228CrossRefGoogle Scholar
  10. Li X (2005) Demosaicing by successive approximation. IEEE Trans Image Process A Publ IEEE Signal Process Soc 14(3):370–379Google Scholar
  11. Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forens Secur 10(3):507–518CrossRefGoogle Scholar
  12. Monaci G, Vandergheynst P (2004) Learning structured dictionaries for image representation. In: International conference on image processing, 2004, vol 4. ICIP ’04. IEEE, pp 2351–2354Google Scholar
  13. Pan Z, Zhang Y, Kwong S (2015) Efficient motion and disparity estimation optimization for low complexity multiview video coding. IEEE Trans Broadcast 61(2):166–176CrossRefGoogle Scholar
  14. Shen H, Zhang L, Huang B, Li P (2007) A map approach for joint motion estimation, segmentation, and super resolution. IEEE Trans Image Process A Publ IEEE Signal Process Soc 16(2):479–490MathSciNetCrossRefGoogle Scholar
  15. Wang X, Tang X (2005) Hallucinating face by eigentransformation. IEEE Trans Syst Man Cybern C 35(3):425–434CrossRefGoogle Scholar
  16. Wang J, Li T, Shi Y-Q, Lian S, Ye J (2016) Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics. Multimed Tools Appl. doi: 10.1007/11042-016-4153-0
  17. Wei W, Liu Z, He X (2011) Learning-based super resolution using kernel partial least squares. Image Vis Comput 29(6):394–406CrossRefGoogle Scholar
  18. Xia Z, Wang X, Zhang L, Qin Z, Sun X, Ren K (2016) A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans Inf Forens Secur 11(11):2594–2608CrossRefGoogle Scholar
  19. Yang J, Wright J, Huang TS, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process A Publ IEEE Signal Process Soc 19(11):2861–2873MathSciNetCrossRefMATHGoogle Scholar
  20. Yang S, Liu Z, Wang M, Sun F, Jiao L (2011) Multitask dictionary learning and sparse representation based single-image super-resolution reconstruction. Neurocomputing 74(17):3193–3203CrossRefGoogle Scholar
  21. Yang X, Liu K, Gan Z, Yan B (2016) Multiscale and multitopic sparse representation for multisensor infrared image superresolution. J Sens 1–14:2015Google Scholar
  22. Zhang L, Zhang H, Shen H, Li P (2010) A super-resolution reconstruction algorithm for surveillance images. Signal Process 90(3):848–859CrossRefMATHGoogle Scholar
  23. Zhang K, Gao X, Li X, Tao D (2011) Partially supervised neighbor embedding for example-based image super-resolution. 5(2):230–239Google Scholar
  24. Zhangjie F, Xinle W, Guan C, Sun X, Ren K (2016) Toward efficient multi-keyword fuzzy search over encrypted outsourced data with accuracy improvement. IEEE Trans Inf Forens Secur 11(12):1–1Google Scholar
  25. Zeyde R, Elad M, Protter M (2010) On single image scale-up using sparse-representations. Springer, BerlinMATHGoogle Scholar
  26. Zhou Z, Wang Y, Jonathan Wu QM, Yang CN, Sun X (2016) Effective and efficient global context verification for image copy detection. IEEE Trans Inf Forens Secur 12(1):48–63CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Xiaomin Yang
    • 1
  • Wei Wu
    • 1
  • Kai Liu
    • 2
  • Weilong Chen
    • 3
  • Zhili Zhou
    • 4
  1. 1.College of Electronics and Information EngineeringSichuan UniversityChengduPeople’s Republic of China
  2. 2.School of Electrical Engineering and InformationSichuan UniversityChengduPeople’s Republic of China
  3. 3.College of Movie and MediaSichuan Normal UniversityChengduPeople’s Republic of China
  4. 4.Jiangsu Engineering Center of Network Monitoring and School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingPeople’s Republic of China

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