DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and Cross-Modality Synthesis in MRI

  • Yawen HuangEmail author
  • Ling Shao
  • Alejandro F. Frangi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)


Cross-modal image synthesis is a topical problem in medical image computing. Existing methods for image synthesis are either tailored to a specific application, require large scale training sets, or are based on partitioning images into overlapping patches. In this paper, we propose a novel Dual cOnvolutional filTer lEarning (DOTE) approach to overcome the drawbacks of these approaches. We construct a closed loop joint filter learning strategy that generates informative feedback for model self-optimization. Our method can leverage data more efficiently thus reducing the size of the required training set. We extensively evaluate DOTE in two challenging tasks: image super-resolution and cross-modality synthesis. The experimental results demonstrate superior performance of our method over other state-of-the-art methods.


Dual learning Convolutional sparse coding 3D Multi-modal Image synthesis MRI 



This work has been partially supported by the European Commission FP7 project VPH-DARE@IT (FP7-ICT-2011-9-601055).


  1. 1.
    Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE TIP 19(11), 2861–2873 (2010)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Timofte, R., De Smet, V., Van Gool, L.: A+: adjusted anchored neighborhood regression for fast super-resolution. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9006, pp. 111–126. Springer, Cham (2015). doi: 10.1007/978-3-319-16817-3_8 CrossRefGoogle Scholar
  3. 3.
    Vemulapalli, R., Van Nguyen, H., Zhou, S.K.: Unsupervised cross-modal synthesis of subject-specific scans. In: IEEE ICCV, pp. 630–638 (2015)Google Scholar
  4. 4.
    Rousseau, F.: Alzheimer disease neuroimaging initiative: a non-local approach for image super-resolution using intermodality priors. MIA 14(4), 594–605 (2010)Google Scholar
  5. 5.
    Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D., Chenin, P., Cohen, A., Gout, C., Lyche, T., Mazure, M.-L., Schumaker, L. (eds.) Curves and Surfaces 2010. LNCS, vol. 6920, pp. 711–730. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-27413-8_47 CrossRefGoogle Scholar
  6. 6.
    Bristow, H., Eriksson, A., Lucey, S.: Fast convolutional sparse coding. In: IEEE CVPR, pp. 391–398 (2013)Google Scholar
  7. 7.
    He, D., Xia, Y., Qin, T., Wang, L., Yu, N., Liu, T., Ma, W.: Dual learning for machine translation. In: NIPS, pp. 820–828 (2016)Google Scholar
  8. 8.
    Gu, S., Zuo, W., Xie, Q., Meng, D., Feng, X., Zhang, L.: Convolutional sparse coding for image super-resolution. In: IEEE ICCV, pp. 1823–1831 (2015)Google Scholar
  9. 9.
    Timofte, R., De Smet, V., Van Gool, L.: Anchored neighborhood regression for fast example-based super-resolution. In: IEEE ICCV, pp. 1920–1927 (2013)Google Scholar
  10. 10.
    Roy, S., Carass, A., Prince, J.L.: Magnetic resonance image example-based contrast synthesis. IEEE TMI 32(12), 2348–2363 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Electronic and Electrical EngineeringThe University of SheffieldSheffieldUK
  2. 2.School of Computing SciencesUniversity of East AngliaNorwichUK

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