In this study, a novel single image super-resolution (SR) method, which uses a generated dictionary from pairs of high-resolution (HR) images and their corresponding low-resolution (LR) representations, is proposed. First, HR and LR dictionaries are created by dividing HR and LR images into patches Afterwards, when performing SR, the distance between every patch of the input LR image and those of available LR patches in the LR dictionary are calculated. The minimum distance between the input LR patch and those in the LR dictionary is taken, and its counterpart from the HR dictionary will be passed through an illumination enhancement process resulting in consistency of illumination between neighbour patches. This process is applied to all patches of the LR image. Finally, in order to remove the blocking effect caused by merging the patches, an average of the obtained HR image and the interpolated image is calculated. Furthermore, it is shown that the stabe of dictionaries is reducible to a great degree. The speed of the system is improved by 62.5%. The quantitative and qualitative analyses of the experimental results show the superiority of the proposed technique over the conventional and state-of-the-art methods.

References

1. 1)
  - 12. Lin, F.C., Fookes, C.B., Chandran, V., et al: ‘Investigation into optical flow super-resolution for surveillance applications’, in Lovell, B. C., Maeder, A. J. (Eds.): ‘APRS Workshop on Digital Image Computing: Pattern Recognition and Imaging for Medical Applications’ (Brisbane), 2005.
2. 2)
  - 38. Hou, H.S., Andrews, H.: ‘Cubic splines for image interpolation and digital filtering’, IEEE Trans. Acoustics Speech Signal Process., 1978, 26, (6), pp. 508–517.
3. 3)
  - 6. Rasti, P., Demirel, H., Anbarjafari, G.: ‘Image resolution enhancement by using interpolation followed by iterative back projection’. 2013 21st Signal Processing and Communications Applications Conf. (SIU), 2013, pp. 1–4.
4. 4)
  - 22. Yang, J., Huang, T.: ‘Image super-resolution: historical overview and future challenges’, Super-Resolution Imaging, 2010, pp. 1–34.
5. 5)
  - 31. Freeman, W.T., Jones, T.R., Pasztor, E.C.: ‘Example-based super-resolution’, Comput. Graph. Appl., 2002, 22, (2), pp. 56–65.
6. 6)
  - 16. Peled, S., Yeshurun, Y.: ‘Super-resolution in mri: application to human white matter fiber tract visualization by diffusion tensor imaging’, Magn. Reson. Med., 2001, 45, (1), pp. 29–35.
7. 7)
  - 15. Malczewski, K., Stasinski, R.: ‘Toeplitz-based iterative image fusion scheme for mri’. 15th IEEE Int. Conf Image Processing 2008, ICIP 2008, 2008, pp. 341–344.
8. 8)
  - 1. Milanfar, P.: ‘Super-resolution imaging’ (CRC Press, 2010).
9. 9)
  - 19. Sezer, O.G., Altunbasak, Y., Ercil, A.: ‘Face recognition with independent component-based superresolution’, Proc. SPIE 6077, Visual Communications and Image Processing, 2006, p. 607705, doi:10.1117/12.645868.
10. 10)
  - 40. Kivinukk, A., Tamberg, G.: ‘On sampling operators defined by the hann window and some of their extensions’, Sampl. Theory Signal Image Process., 2003, 2, pp. 235–258.
11. 11)
  - 36. Yu, G., Sapiro, G., Mallat, S.: ‘Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity’, IEEE Trans. Image Process., 2012, 21, (5), pp. 2481–2499.
12. 12)
  - 20. Gunturk, B.K., Batur, A.U., Altunbasak, Y., et al: ‘Eigenface-domain super-resolution for face recognition’, IEEE Trans. Image Process., 2003, 12, (5), pp. 597–606.
13. 13)
  - 4. Rasti, P., Nasrollahi, K., Orlova, O., et al: ‘Reducible dictionaries for single image super-resolution based on patch matching and mean shifting’, J. Electron. Imaging, 2017, 26, (2), p. 023024.
14. 14)
  - 7. Demirel, H., Anbarjafari, G.: ‘Discrete wavelet transform-based satellite image resolution enhancement’, IEEE trans. geoscience. and remote sensing, 2011, 49, (6), pp. 1997–2004.
15. 15)
  - 10. Rasti, P., Lusi, I., Demirel, H., et al: ‘Wavelet transform based new interpolation technique for satellite image resolution enhancement’. IEEE Int. Conf. Aerospace Electronics and Remote Sensing Technology (ICARES), 2014, 2014, pp. 185–188.
16. 16)
  - 14. Maintz, J.A., Viergever, M.A.: ‘A survey of medical image registration’, Med. Image Anal., 1998, 2, (1), pp. 1–36.
17. 17)
  - 29. Kim, K.I., Kwon, Y.: ‘Example-based learning for single-image super-resolution’, in ‘Joint Pattern Recognition Symposium’ (Springer, Berlin, Heidelberg, 2008), pp. 456–465.
18. 18)
  - 27. Zeyde, R., Elad, M., Protter, M.: ‘On single image scale-up using sparse-representations’, in ‘International conference on curves and surfaces’ (Springer, Berlin, Heidelberg, 2010), pp. 711–730.
19. 19)
  - 41. Kivinukk, A., Tamberg, G.: ‘On Blackman-Harris windows for Shannon sampling series’, Sampling Theory in Signal Image Process., 2007, 6, (1), pp. 87–108.
20. 20)
  - 18. Hennings-Yeomans, P.H., Baker, S., Kumar, B.V.: ‘Recognition of low-resolution faces using multiple still images and multiple cameras’. 2nd IEEE Int. Conf. Biometrics: Theory, Applications and Systems 2008, BTAS 2008, 2008, pp. 1–6.
21. 21)
  - 23. Peleg, T., Elad, M.: ‘A statistical prediction model based on sparse representations for single image super-resolution’, IEEE Trans. Image Process., 2014, 23, (6), pp. 2569–2582.
22. 22)
  - 35. Tang, Y., Chen, H.: ‘Matrix-value regression for single-image super-resolution’. Int. Conf. Wavelet Analysis and Pattern Recognition (ICWAPR), 2013, 2013, pp. 215–220.
23. 23)
  - 17. Capel, D., Zisserman, A.: ‘Super-resolution enhancement of text image sequences’. Proc. 15th Int. Conf. Pattern Recognition 2000, 2000, vol. 1, pp. 600–605.
24. 24)
  - 28. Zhu, Z., Guo, F., Yu, H., et al: ‘Fast single image super-resolution via self-example learning and sparse representation’, IEEE Trans. Multimed., 16, (8), 2014, pp. 2178–2190.
25. 25)
  - 42. Schmeisser, G.: ‘Interconnections between multiplier methods and window methods in generalized sampling’, Sampl. Theory Signal Image Process., 2010, 9, pp. 1–24.
26. 26)
  - 44. ‘Standard test image’, accessed: 2016-04-05.
27. 27)
  - 25. Rasti, P., Orlova, O., Tamberg, G., et al: ‘Improved interpolation kernels for super resolution algorithms’. 6th Int. Conf. Image Processing Theory Tools and Applications (IPTA), 2016, 2016, pp. 1–6.
28. 28)
  - 11. Cristani, M., Cheng, D.S., Murino, V., et al: ‘Distilling information with super-resolution for video surveillance’. Proc. ACM 2nd Int. Workshop on Video Surveillance & Sensor Networks, 2004, pp. 2–11.
29. 29)
  - 34. Tang, Y., Chen, H., Liu, Z., et al: ‘Example-based super-resolution via social images’, Neurocomputing, 2016, 172, pp. 38–47.
30. 30)
  - 13. Kennedy, J.A., Israel, O., Frenkel, A., et al: ‘Super-resolution in pet imaging’, IEEE Trans. Med. Imaging, 2006, 25, (2), pp. 137–147.
31. 31)
  - 43. Butzer, P.L., Nessel, R.J.: ‘Fourier analysis and approximation’ vol. 1 (Stuttgart: Birkhäuser Verlag, Basel, 1971).
32. 32)
  - 39. Kivinukk, A., Tamberg, G.: ‘Interpolating generalized Shannon sampling operators, their norms and approximation properties’, Sampl. Theory Signal Image Process., 2009, 8, pp. 77–95.
33. 33)
  - 8. Huang, T.S.: ‘Multiple frame image restoration and registration’, Adv. Comput. Vis. Image Process., 1, 1984, pp. 317–339.
34. 34)
  - 5. Yang, J., Huang, T.: ‘Image super-resolution: Historical overview and future challenges’, Super-Resolution Imaging, 2010, pp. 20–34.
35. 35)
  - 32. Ram, S., Rodr, J.J.: ‘Image super-resolution using graph regularized block sparse representation’. 2016 IEEE Southwest Symp. Image Analysis and Interpretation (SSIAI), 2016, pp. 69–72.
36. 36)
  - 37. Keys, R.G.: ‘Cubic convolution interpolation for digital image processing’, IEEE Trans. Acoustics Speech Signal Process., 1981, 29, (6), pp. 1153–1160.
37. 37)
  - 48. Wang, Z., Liu, D., Yang, J., et al: ‘Deep networks for image super-resolution with sparse prior’. Proc. IEEE Int. Conf. Computer Vision, 2015, pp. 370–378.
38. 38)
  - 46. Yang, C.-Y., Ma, C., Yang, M.-H.: ‘Single-image super-resolution: a benchmark’. Computer Vision–ECCV 2014, 2014, pp. 372–386.
39. 39)
  - 30. Kim, K.I., Kim, D., Kim, J.H.: ‘Example-based learning for image super-resolution’, Proc. the third Tsinghua-KAIST Joint Workshop on Pattern Recognition, 2004, pp. 140–148.
40. 40)
  - 47. Timofte, R., Smet, V., Gool, L.: ‘Anchored neighborhood regression for fast example-based superresolution’. Proc. IEEE Int. Conf. Computer Vision, 2013, pp. 1920–1927.
41. 41)
  - 21. Nasrollahi, K., Moeslund, T.B.: ‘Finding and improving the key-frames of long video sequences for face recognition’. Fourth IEEE Int. Conf. Biometrics: Theory Applications and Systems (BTAS), 2010, September 2010, pp. 1–6.
42. 42)
  - 2. Nasrollahi, K., Moeslund, T.B.: ‘Super-resolution: a comprehensive survey’, Mach. Vis. Appl., 2014, 25, (6), pp. 1423–1468.
43. 43)
  - 9. Li, F., Jia, X., Fraser, D.: ‘Universal hmt based super resolution for remote sensing images’. 15th IEEE Int. Conf. Image Processing, 2008, ICIP 2008, 2008, pp. 333–336.
44. 44)
  - 45. Huang, G.B., Mattar, M., Lee, H., et al: ‘Learning to align from scratch’. NIPS, 2012.
45. 45)
  - 24. Dong, C., Loy, C.C., He, K., et al: ‘Image super-resolution using deep convolutional networks’, 2015.
46. 46)
  - 33. Ram, S., Rodriguez, J.J.: ‘Single image super-resolution using dictionary-based local regression’. IEEE Southwest Symp. Image Analysis and Interpretation (SSIAI), 2014, 2014, pp. 121–124.
47. 47)
  - 26. Yang, J., Wright, J., Huang, T., et al: ‘Image super-resolution as sparse representation of raw image patches’. IEEE Conf. Computer Vision and Pattern Recognition 2008, CVPR 2008, 2008, pp. 1–8.
48. 48)
  - 3. Demirel, H., Anbarjafari, G.: ‘Image resolution enhancement by using discrete and stationary wavelet decomposition’, IEEE Trans. Image Process., 2011, 20, (5), pp. 1458–1460.

A new low-complexity patch-based image super-resolution

References

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