Single image super-resolution using coupled dictionary learning and cross domain mapping

  • Hemant S. Goklani
  • Shravya S.
  • Jignesh N. Sarvaiya
Article
  • 116 Downloads

Abstract

In this paper, a new algorithm for single image super resolution using coupled wavelet and spatial domain dictionary pairs is proposed. The standard deviation parameter, which is approximately scale invariant for low and high resolution patch pairs is employed for clustering. A pair of online coupled dictionaries is learned for each cluster using a low resolution image. The standard deviation measure of a low resolution patch is used to select the appropriate cluster dictionary pair for reconstructing the high resolution counterpart. Experimental results show that the performance of the proposed algorithm is superior to the existing methods in terms of objective and subjective quality measures. The objective image quality is measured in terms of PSNR and SSIM. This paper also proposed an extended algorithm, based on selective sparse representation over a set of coupled dictionary pair. The extended algorithm applies the coupled dictionary based sparse framework for patches having high standard deviation. Whereas, low complexity patch collaging method is used to super resolve low standard deviation valued patches. It is found empirically that a large percentage of patches have low standard deviation values. Moreover, the selective approach significantly reduces the computational complexity without losing the overall reconstruction quality.

Keywords

Single image super-resolution Clustering Standard deviation Sparse coding Coupled dictionary learning 

Notes

Acknowledgments

It gives us immense pleasure to thank the anonymous reviewers for their careful reading of our manuscript and their insightful comments and valuable suggestions that helped us to improve the quality of manuscript. We thank Dr. Jigisha N. Patel, Dr. Mukesh A. Zaveri and Dr. Jigar H. Shah for their cooperation and guidance throughout the work. We also thank Pinal. J. Engineer, for his support.

References

  1. 1.
    Aharon M, Elad M, Bruckstein A (2006) K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing 54:4311–4322CrossRefGoogle Scholar
  2. 2.
    Dai D, Timofte R, Van Gool L (2015) Jointly optimized regressors for image super-resolution. Journal Computer Graphics Forum 34(2):95–104CrossRefGoogle Scholar
  3. 3.
    Dong W, Zhang L, Shi G, Wu X (2011) Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. In: IEEE Trans Ind Appl, vol 20, pp 1838–1857Google Scholar
  4. 4.
    Dong W, Zhang L, Lukac R, Shi G (2013) Sparse representation based image interpolation with nonlocal autoregressive modeling. IEEE Trans Image Process 22(4):1382–1394MathSciNetCrossRefGoogle Scholar
  5. 5.
    Dong C, Loy CC, He K, Tang X (2016) Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 38(2):295–307CrossRefGoogle Scholar
  6. 6.
    Engan K, Aase SO, Hakon Husoy J (1999) Method of optimal directions for frame design. In: Proceedings 1999 IEEE international conference on acoustics, speech, and signal processing, vol 5, Phoenix, pp 2443–2446Google Scholar
  7. 7.
    Freeman WT, Pasztor EC, Carmichael OT (2000) Learning low-level vision. Int J Comput Vis 40(1):25–47CrossRefMATHGoogle Scholar
  8. 8.
    Huang DA, Wang YCF (2013) Coupled dictionary and feature space learning with applications to cross-domain image synthesis and recognition. In: 2013 IEEE international conference on computer vision. NSW, Sydney, pp 2496–2503CrossRefGoogle Scholar
  9. 9.
    Keys R (1981) Cubic convolution interpolation for digital image processing. IEEE Transactions on Acoustics, Speech and Signal Processing 29:1153–1160MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Lee H, Battle A, Raina R, Ng AY (2006) Efficient sparse coding algorithms. In: Advances in neural information processing systems, pp 801–808Google Scholar
  11. 11.
    Leung BO, Chou KC (2011) Review of Super-Resolution fluorescence microscopy for biology. Appl Spectrosc 65:967–980CrossRefGoogle Scholar
  12. 12.
    Li H, Qi H, Zaretzki R (2013) Beta process joint dictionary learning for coupled feature spaces with application to single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognitionGoogle Scholar
  13. 13.
    Li Y, Li F, Bai B, Shen Q (2016) Image fusion via nonlocal sparse k-SVD dictionary learning. Appl Opt 55:1814–1823CrossRefGoogle Scholar
  14. 14.
    Li Q, Zhou X, Gu A, Li Z, Liang RZ (2016) Nuclear norm regularized convolutional Max Pos@ Top machine. In: Neural computing and applications. Springer, London, pp 1–10Google Scholar
  15. 15.
    Liang Ru-Ze, Xie Wei, Li W, Wang H, Wang JJ-Y, Taylor L (2016) A novel transfer learning method based on common space mapping and weighted domain matching. In: IEEE 28th international conference on tools with artificial intelligence (ICTAI), pp 299–303Google Scholar
  16. 16.
    Liang R-Z, Shi L, Wang H, Meng J, Wang JJ-Y, Sun Q, Gu Y (2016) Optimizing top precision performance measure of content-based image retrieval by learning similarity function. In: 23st International Conference on Pattern Recognition (ICPR), pp 2954–2958CrossRefGoogle Scholar
  17. 17.
    Mairal J, Bach F, Ponce J, Sapiro G (2009) Online dictionary learning for sparse coding. In: Proceedings of the 26th annual international conference on machine learning, ICML ’09, vol 689–696. ACM, New YorkGoogle Scholar
  18. 18.
    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 Computer Vision, 2001. ICCV 2001. Eighth IEEE international conference on proceedings 2:416–423Google Scholar
  19. 19.
    Mallat S (1999) A wavelet tour of signal processing. Academic pressGoogle Scholar
  20. 20.
    Marcellin M, Gormish M, Bilgin A, Boliek M (2000) An overview of jpeg-2000. In: Proceedings DCC 2000, Data compression conference, pp 523–541CrossRefGoogle Scholar
  21. 21.
    Nazzal Mahmoud, Ozkaramanli Huseyin (2013) Improved single image super-resolution using sparsity and structured dictionary learning in wavelet domain. 21st. IEEE Signal Processing and Communications Applications Conference (SIU)Google Scholar
  22. 22.
    Nazzal M, Ozkaramanli H (2015) Wavelet domain dictionary learning-based single image superresolution. SIViP 9(7):1491–1501CrossRefGoogle Scholar
  23. 23.
    Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381:607–609CrossRefGoogle Scholar
  24. 24.
    Soman KP, Ramachandran KI (2006) Insight into Wavelets,from theory to practice. Prentice-Hall of India Private LimitedGoogle Scholar
  25. 25.
    Sun J, Zheng N-N, Tao H, Shum H-Y (2003) Image hallucination with primal sketch priors. In: IEEE Proceedings of Computer Society Conference on Computer Vision and Pattern Recognition, vol 2Google Scholar
  26. 26.
    Sun J, Sun J, Xu Z, Shum HY (2011) Gradient profile prior and its applications in image super-resolution and enhancement. IEEE Trans Image Process 20:1529–1542MathSciNetCrossRefGoogle Scholar
  27. 27.
    Sun D, Gao Q, Lu Y (2016) Image interpolation via collaging its non-local patches. Digital Signal Processing 49:33–43CrossRefGoogle Scholar
  28. 28.
    Suryanarayana G, Dhuli R (2016) Simultaneous edge preserving and Noise Mitigating Image Super-resolution algorithm. In: AEU-international journal of electronics and communicationsGoogle Scholar
  29. 29.
    Tao D, Guo Y, Song M, Li Y, Yu Z, Tang YY (2016) Person re-identification by dual-regularized KISS metric learning. IEEE Trans Image Process 25(6):2726–2738MathSciNetCrossRefGoogle Scholar
  30. 30.
    Tao D, Lin X, Jin L, Li X (2016) Principal component 2-D long short-term memory for font recognition on single chinese characters. IEEE Transactions on Cybernetics 46(3):756–765.  https://doi.org/10.1109/TCYB.2015.2414920 CrossRefGoogle Scholar
  31. 31.
    Tao D, Cheng J, Gao X, Li X, Deng C (2017) Robust sparse coding for mobile image labeling on the cloud. IEEE Transactions on Circuits and Systems for Video Technology 27(1):62–72CrossRefGoogle Scholar
  32. 32.
    Taubman DS, Marcellin M (2001) JPEG 2000: image compression fundamentals, standards and practice, vol 2000. Kluwer Academic Publishers, NorwellGoogle Scholar
  33. 33.
    Timofte R, Smet Vt, Gool L (2013) Anchored neighborhood regression for fast example-based super-resolution. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1920–1927Google Scholar
  34. 34.
    Timofte R, De Smet V, Van Gool L (2014) A+: adjusted anchored neighborhood regression for fast super-resolution. In: Computer vision–ACCV 2014. Springer International Publishing, pp 111–126Google Scholar
  35. 35.
    Timofte R, Rothe R, Van Gool L (2015) Seven ways to improve example-based single image super resolution”, arXiv preprint arXiv:1511.02228
  36. 36.
    Viola P, Jones M (2001) Robust real-time object detection. Int J Comput Vis 4Google Scholar
  37. 37.
    Walha R, et al (2013) Multiple learned dictionaries based clustered sparse coding for the super-resolution of single text image. In: 12th international conference on document analysis and recognition (ICDAR), pp 484–488Google Scholar
  38. 38.
    Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: From error visibility to structural similarity. Transaction on Image Processing (TIP) 13(4):600–612. IEEE pressCrossRefGoogle Scholar
  39. 39.
    Wang S, Zhang L, LY, Pan Q (2012) Semi-coupled dictionary learning with applications in image super-resolution and photo-sketch synthesis. In: International conference on computer vision and pattern recognition (CVPR). IEEEGoogle Scholar
  40. 40.
    Wang F, Lai HSS, Liu L, Li P, Yu H, Liu Z, Wang Y, Li WJ (2015) Super-resolution endoscopy for real-time wide-field imaging. Opt Express 23:16803–16811CrossRefGoogle Scholar
  41. 41.
    Weber AG (1997) The USC-SIPI Image Database, tech. rep., University of Southern California, Signal and Image Processing Institute, Department of Electrical Engineering, Los Angeles, CA 90089-2564 USA, 3740 McClintock AveGoogle Scholar
  42. 42.
    Yang J, Wright J, Huang T, Ma Y (2008) Image super-resolution as sparse representation of raw image patches. In: IEEE conference on computer vision and Pattern recognition (CVPR), pp 1–8Google Scholar
  43. 43.
    Yang J, Wright J, Huang T, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19:2861–2873MathSciNetCrossRefGoogle Scholar
  44. 44.
    Yang J, Wang Z, Lin Z, Cohen S, Huang T (2012) Coupled dictionary training for image super-resolution. IEEE Transactions on Image Processing 21(8):3467–3478MathSciNetCrossRefGoogle Scholar
  45. 45.
    Yang S, et al (2012) Single-image super-resolution reconstruction via learned geometric dictionaries and clustered sparse coding. IEEE Trans Image Process 21 (9):4016–4028MathSciNetCrossRefGoogle Scholar
  46. 46.
    Zeyde R, Elad M, Protter M (2010) On single image scale-up using sparse-representations. In: Curves and surfaces. Springer, Berlin, pp 711–730Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Electronics EngineeringSardar Vallabhbhai National Institute of Technology (SVNIT)SuratIndia

Personalised recommendations