Lorentzian Norm Based Super-Resolution Reconstruction of Brain MRI Image

  • Dongxing Bao
  • Xiaoming Li
  • Jin LiEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 227)


Nowadays, SRR (super resolution image reconstruction) technology is a very effective method in improving spatial resolution of images and obtaining high-definition images. The SRR approach is an image late processing method that does not require any improvement in the hardware of the imaging system. In the SRR reconstruction model, it is the key point of the research to choose a proper cost function to achieve good reconstruction effect. In this paper, based on a lot of research, Lorenzian norm is employed as the error term, Tikhonov regularization is employed as the regularization term in the reconstruction model, and iteration method is employed in the process of SRR. In this way, the outliers and image edge preserving problems in SRR reconstruction process can be effectively solved and a good reconstruction effect can be achieved. A low resolution MRI brain image sequence with motion blur and several noises are used to test the SRR reconstruction algorithm in this paper and the reconstruction results of SRR reconstruction algorithm based on L2 norm are also be used for comparison and analysis. Results from experiments show that the SRR algorithm in this paper has better practicability and effectiveness.


MRI image Super resolution image reconstruction Lorentzian norm Regularization Iteration 


  1. 1.
    Tsai, R.Y., Huang, T.S.: Multi-frame image restoration and registration. Adv. Comput. Vis. Image Process. 1(2), 317–339 (1984)Google Scholar
  2. 2.
    Rhee, S., Kang, M.G.: Discrete cosine transform based regularized high-resolution image reconstruction algorithm. Opt. Eng. 38(8), 1348–1356 (1999)CrossRefGoogle Scholar
  3. 3.
    Nguyen, N., Milanfar, P.: A wavelet-based interpolation restoration method for superresolution. Circ. Syst. Sig. Process. 19(4), 321–338 (2000)CrossRefzbMATHGoogle Scholar
  4. 4.
    Irani, M., Peleg, S.: Improving resolution by image registration. CVGIP: Graph. Models Image Process. 53(3), 231–239 (1991)Google Scholar
  5. 5.
    Ur, H., Gross, D.: Improved resolution from subpixel shifted pictures. CVGIP: Graph. Models Image Process. 54(2), 181–186 (1992)Google Scholar
  6. 6.
    Hardie, R.C., Barnard, K.J., Armstrong, E.E.: Joint MAP registration and high-resolution image estimation using a sequence of undersampled images. IEEE Trans. Image Process. 6(12), 1621–1633 (1997)CrossRefGoogle Scholar
  7. 7.
    Tom, B.C., Katsaggelos, A.K.: Reconstruction of a high resolution image from multiple-degraded misregistered low resolution images. In: Proceedings of SPIE, Visual Communications and Image Processing, vol. 2308, pp. 971–981 (1994)Google Scholar
  8. 8.
    Patti, A.J., Sezan, M.I., Tekalp, A.M.: High-resolution image reconstruction from a low-resolution image sequence in the presence of time-varying motion blur. In: IEEE International Conference Image Processing (ICIP 1994), vol. 1, pp. 343–347 (1994)Google Scholar
  9. 9.
    Elad, M., Feuer, A.: Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images. IEEE Trans. Image Process. 6(12), 1646–1658 (1997)CrossRefGoogle Scholar
  10. 10.
    Elad, M., Feuer, A.: Superresolution restoration of an image sequence: adaptive filtering approach. IEEE Trans. Image Process. 8(3), 387–395 (1999)CrossRefGoogle Scholar
  11. 11.
    Woods, N.A., Galatsanos, N.P., Katsaggelos, A.K.: Stochastic methods for joint registration, restoration, and interpolation of multiple undersampled images. IEEE Trans. Image Process. 15(1), 201–213 (2006)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Farsiu, S., Elad, M., Milanfar, P.: Multiframe demosaicing and super-resolution of color images. IEEE Trans. Image Process. 15(1), 141–159 (2006)CrossRefGoogle Scholar
  13. 13.
    Tsaig, Y., Donoho, D.L.: Extensions of compressed sensing. Sig. Process. 86(3), 549–571 (2006)CrossRefzbMATHGoogle Scholar
  14. 14.
    Yang, J., Lin, Z., Cohen, S.: Fast image super-resolution based on in-place example regression. In: Computer Vision Foundation (2013)Google Scholar
  15. 15.
    Patanavijit, V., Jitapunkul, S.: A Lorentzian stochastic estimation for a robust iterative multiframe super-resolution reconstruction with Lorentzian-Tikhonov regularization. EURASIP J. Adv. Sig. Process. 2007, 1–21 (2007)zbMATHGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.College of AutomationHarbin Engineering UniversityHarbinChina
  2. 2.School of Electronic EngineeringHeilongjiang UniversityHarbinChina
  3. 3.Department of Microelectronics Science and TechnologyHarbin Institute of TechnologyHarbinChina

Personalised recommendations