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Single MR-image super-resolution based on convolutional sparse representation

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Abstract

In this paper, a method is proposed to achieve a high-resolution image from a low-resolution image. Because of the ill-posedness of the super-resolution problem, sparsity constraint is used as a prior, in this work. On the one hand, we use convolutional sparse representation on the whole image different from the patch-based method. On the other hand, we apply fewer filters even in smaller sizes for reconstructing the high-resolution image. Therefore, despite the reduced processing time, the reconstructed image quality is improved compared to the reference methods. In this work, the training images are different in terms of content from the testing images. Experimental results on a variety of MR images indicate improvement in the quality of the high-resolution MR image, in terms of qualitative and quantitative criteria.

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References

  1. Yue, L., Shen, H., Li, J., Yuan, Q., Zhang, H., Zhang, L.: Image super-resolution: the techniques, applications, and future. Sig. Process. 128, 389–408 (2016)

    Article  Google Scholar 

  2. Wang, H., Gao, X., Zhang, K., Li, J.: Single-image super-resolution using active-sampling Gaussian process regression. IEEE Trans. Image Process. 25(2), 935–948 (2016)

    Article  MathSciNet  Google Scholar 

  3. Huang, S., Sun, J., Yang, Y., Fang, Y., Lin, P., Que, Y.: Robust single-image super-resolution based on adaptive edge-preserving smoothing regularization. IEEE Trans. Image Process. 27(6), 2650–2663 (2018)

    Article  MathSciNet  Google Scholar 

  4. Peleg, T., Elad, M.: A statistical prediction model based on sparse representations for single image super-resolution. IEEE Trans. Image Process. 23(6), 2569–2582 (2014)

    Article  MathSciNet  Google Scholar 

  5. Tang, S., Guo, H., Zhou, N., Huang, L., Zhan, T.: Coupled dictionary learning on common feature space for medical image super resolution. In: IEEE Conference on Image Processing (ICIP), pp. 574–578 (2016)

  6. Rueda, A., Malpica, N., Romero, E.: Single-image super-resolution of brain MR images using overcomplete dictionaries. Med. Image Anal. 17(1), 113–132 (2013)

    Article  Google Scholar 

  7. Xiaole, Z., Zhang, H., Liu, H., Qin, Y., Zhang, T., Zou, X.: Single MR image super-resolution via channel splitting and serial fusion network. arXiv preprint arXiv:1901.06484 (2019)

  8. Bazzi, F., Mescam, M., Basarab, A., Kouamé, D.: On single-image super-resolution in 3d brain magnetic resonance imaging. In: 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2840–2843 (2019)

  9. Lu, X., Huang, Z., Yuan, Y.: MR image super-resolution via manifold regularized sparse learning. Neurocomputing 162, 96–104 (2015)

    Article  Google Scholar 

  10. Farsiu, S., Robinson, M.D., Elad, M., Milanfar, P.: Fast and robust multiframe super resolution. IEEE Trans. Image Process. 13(10), 1327–1344 (2004)

    Article  Google Scholar 

  11. Yuan, Q., Zhang, L., Shen, H.: Multiframe super-resolution employing a spatially weighted total variation model. IEEE Trans. Circuits Syst. Video Technol. 22(3), 379–392 (2012)

    Article  Google Scholar 

  12. Kawulok, M., Benecki, P., Piechaczek, S., Hrynczenko, K., Kostrzewa, D., Nalepa, J.: DL for multiple-image super-resolution. arXiv preprint arXiv:1903.00440 (2019)

  13. Molini, A.B., Valsesia, D., Fracastoro, G., Magli, E.: DeepSUM: deep neural network for super-resolution of unregistered multitemporal images. arXiv preprint arXiv:1907.06490 (2019)

  14. Zhang, K., Gao, X., Tao, D., Li, X.: Single image super-resolution with non-local means and steering kernel regression. IEEE Trans. Image Process. 21(11), 4544–4556 (2012)

    Article  MathSciNet  Google Scholar 

  15. Tsai, R.Y., Huang, T.S., Tsai, R.Y., Huang, T.S.: Multiframe image restoration and registration. Adv. Comput. Vis. Image Process. 1, 317–339 (1984)

    Google Scholar 

  16. Rhee, S., Kang, M.G.: Discrete cosine transform based regularized high-resolution image reconstruction algorithm. Opt. Eng. 38(8), 1348–1356 (1999)

    Article  Google Scholar 

  17. Demirel, H., Izadpanahi, S., Anbarjafari, G.: Improved motion-based localized super resolution technique using discrete wavelet transform for low resolution video enhancement. In: European Signal Processing Conference, pp. 1097–1101 (2009)

  18. Alam, M.S., Bognar, J.G., Hardie, R.C., Yasuda, B.J.: Infrared image registration and high-resolution reconstruction using multiple translationally shifted aliased video frames. IEEE Trans. Instrum. Meas. 49(5), 915–923 (2000)

    Article  Google Scholar 

  19. Wan, B., Meng, L., Ming, D., Qi, H., Hu, Y., Luk, K.D.K.: Video image super-resolution restoration based on iterative back-projection algorithm. In: IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, pp. 46–49 (2009)

  20. Stark, H., Oskoui, P.: High-resolution image recovery from image plane arrays, using convex projections. J. Opt. Soc. Am. A-Opt. Image Sci. Vis. 6(11), 1715–1726 (1989)

    Article  Google Scholar 

  21. Zhu, Z., Guo, F., Yu, H., Chen, C.: Fast single image super-resolution via self-example learning and sparse representation. IEEE Trans. Multimedia 16(8), 2178–2190 (2014)

    Article  Google Scholar 

  22. Nazren, A., Rahim, A., Yaakob, Sh.N., Ngadiran, R., Nasruddin, M.W.: An analysis of interpolation methods for super resolution images. In: IEEE Student Conference on Research and Development, pp. 72–77 (2015)

  23. Wan, X.F., Yang, Y.: Super-resolution image reconstruction. In: International Conference on Computer Application and System Modeling (ICCASM), vol. 8, pp. 351–355 (2010)

  24. Protter, M., Elad, M.: Super resolution with probabilistic motion estimation. IEEE Trans. Image Process. 18(8), 1899–1904 (2009)

    Article  MathSciNet  Google Scholar 

  25. Sun, J., Xu, Z., Shum, H.Y.: Image super-resolution using gradient profile prior. In: Proceedings on IEEE Conference Computer Vision Pattern Recognition, pp. 1–8 (2008)

  26. Tai, Y.W., Liu, S., Brown, M.S., Lin, S.: Super resolution using edge prior and single image detail synthesis. In: Proceedings on IEEE Conference Computer Vision Pattern Recognition, pp. 2400–2407 (2010)

  27. Sun, J., Xu, Z., Shum, H.Y.: Gradient profile prior and its applications in image super-resolution and enhancement. IEEE Trans. Image Process. 20(6), 1529–1542 (2011)

    Article  MathSciNet  Google Scholar 

  28. Zhang, Y., Liu, J., Yang, W., Guo, Z.: Image super-resolution based on structure-modulated sparse representation. IEEE Trans. Image Process. 24(9), 2797–2810 (2015)

    Article  MathSciNet  Google Scholar 

  29. Elad, M.: Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. Springer, Berlin (2010)

    Book  Google Scholar 

  30. Freeman, W.T., Liu, C.: Markov random fields for super-resolution and texture synthesis. In: Blake, A., Kohli, P., Rother, C. (eds.) Advances in Markov Random Fields for Vision and Image Processing. MIT Press, Cambridge (2011)

    Google Scholar 

  31. Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. In: Proceedings on IEEE Conference Computer Vision Pattern Recognition, pp. I–I (2004)

  32. Jiang, J., Ma, X., Chen, C., Lu, T., Wang, Z., Ma, J.: Single image super-resolution via locally regularized anchored neighborhood regression and nonlocal means. IEEE Trans. Multimedia 19(1), 15–26 (2017)

    Article  Google Scholar 

  33. Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)

    Article  MathSciNet  Google Scholar 

  34. Shuaifang, W., Xinzhi, Zh, Wei, W., Qiang, P., Qionghua, W., Xiaomin, Y.: Medical image super-resolution by using multi-dictionary and random forest. Sustain. Cities Soc. 37, 358–370 (2018)

    Article  Google Scholar 

  35. Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R.: Deconvolutional networks. In: Proceedings on IEEE Conference Computer Vision Pattern Recognition, pp. 2528–2535 (2010)

  36. Bristow, H., Eriksson, A., Lucey, S.: Fast convolutional sparse coding. In: Proceedings on IEEE Conference Computer Vision Pattern Recognition, pp. 391–398 (2013)

  37. Wohlberg, B.: Efficient convolutional sparse coding. In: Proceedings on IEEE ICASSP, pp. 7173–7177 (2014)

  38. Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2011)

    Article  Google Scholar 

  39. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2015)

    Article  Google Scholar 

  40. Shuhang, G., Zuo, W., Xie, Q., Meng, D., Feng, X., Zhang, L.: Convolutional sparse coding for image super-resolution. In: IEEE Conference on Computer Vision, pp. 1823–1831 (2015)

  41. Rubinstein, R., Peleg, T., Elad, M.: Analysis K-SVD: a dictionary learning algorithm for the analysis sparse model. IEEE Trans. Signal Process. 61(3), 661–677 (2013)

    Article  MathSciNet  Google Scholar 

  42. Garcia-Cardona, C., Wohlberg, B.: Convolutional dictionary learning: a comparative review and new algorithms. IEEE Trans. Comput. Imaging 4(3), 366–381 (2018)

    Article  MathSciNet  Google Scholar 

  43. Liu, J., Garcia-Cardona, C., Wohlberg, B., Yin, W.: First- and second-order methods for online convolutional dictionary learning. SIAM J. Imaging Sci. 11(2), 1589–1628 (2018)

    Article  MathSciNet  Google Scholar 

  44. Zhong, L.W., Kwok, J.T.: Fast stochastic alternating direction method of multipliers. In: International Conference on Machine Learning (ICML), pp. 46–54 (2014)

  45. Siemens MRI: https://www.siemens-healthineers.com. Accessed Aug 2019

  46. SheikhH, H.R., Bovik, A.C., De Veciana, G.: An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans. Image Process. 14(12), 2117–2128 (2005)

    Article  Google Scholar 

  47. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  48. Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2012)

    Article  Google Scholar 

  49. Liu, L., Liu, B., Huang, H., Bovik, A.C.: No-reference image quality assessment based on spatial and spectral entropies. Sig. Process. Image Commun. 29(8), 856–863 (2014)

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge the funding support of Babol Noshirvani University of Technology through Grant Program No. BNUT/389079/98-5. The authors acknowledge the contribution of Mr. Amirhossein Alinezhad Besheli (Radiology Technician and Medical Intern) and colleagues for helping to MOS analysis.

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  1. Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran

    Shima Kasiri & Mehdi Ezoji

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  1. Shima Kasiri
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  2. Mehdi Ezoji
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Correspondence to Mehdi Ezoji.

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Kasiri, S., Ezoji, M. Single MR-image super-resolution based on convolutional sparse representation. SIViP 14, 1525–1533 (2020). https://doi.org/10.1007/s11760-020-01698-0

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