Skip to main content

Image Super-Resolution by Supervised Adaption of Patchwise Self-similarity from High-Resolution Image

  • Conference paper
  • First Online:
Patch-Based Techniques in Medical Imaging (Patch-MI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9467))

Included in the following conference series:


Image super-resolution is of great interest in medical imaging field. However, different from natural images studied in computer vision field, the low-resolution (LR) medical imaging data is often a stack of high-resolution (HR) 2D slices with large slice thickness. Consequently, the goal of super-resolution for medical imaging data is to reconstruct the missing slice(s) between any two consecutive slices. Since some modalities (e.g., T1-weighted MR image) are often acquired with high-resolution (HR) image, it is intuitive to harness the prior self-similarity information in the HR image for guiding the super-resolution of LR image (e.g., T2-weighted MR image). The conventional way is to find the profile of patchwise self-similarity in the HR image and then use it to reconstruct the missing information at the same location of LR image. However, the local morphological patterns could vary significantly across the LR and HR images, due to the use of different imaging protocols. Therefore, such direct (un-supervised) adaption of self-similarity profile from HR image is often not effective in revealing the actual information in the LR image. To this end, we propose to employ the existing image information in the LR image to supervise the estimation of self-similarity profile by requiring it not only being optimal in representing patches in the HR image, but also producing less reconstruction errors for the existing image information in the LR image. Moreover, to make the anatomical structures spatially consistent in the reconstructed image, we simultaneously estimate the self-similarity profiles for a stack of patches across consecutive slices by solving a group sparse patch representation problem. We have evaluated our proposed super-resolution method on both simulated brain MR images and real patient images with multiple sclerosis lesion, achieving promising results with more anatomical details and sharpness.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  1. Rousseau, F.: A non-local approach for image super-resolution using intermodality priors. Med. Image Anal. 14, 594–605 (2010)

    Article  Google Scholar 

  2. Tong, T., Wolz, R., Coupé, P., Hajnal, J., Rueckert, D.: Segmentation of MR images via discriminative dictionary learning and sparse coding: application to hippocampus labeling. NeuroImage 76, 11–23 (2013)

    Article  Google Scholar 

  3. Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 58(1), 267–288 (1996)

    MATH  MathSciNet  Google Scholar 

  4. Liu, J., Ji, S., Ye, J.: Multi-task feature learning via efficient L2,1-norm minimization. In: Proceeding of the 25th Conference on Uncertainty in Artificial Intelligence, Montreal, Canada (2012)

    Google Scholar 

  5. Liu, J., Ji, S., Ye, J.: SLEP: sparse learning with efficient projections. Arizona State University (2009)

    Google Scholar 

  6. 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, 600–612 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Guorong Wu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Wu, G., Zhu, X., Wang, Q., Shen, D. (2015). Image Super-Resolution by Supervised Adaption of Patchwise Self-similarity from High-Resolution Image. In: Wu, G., Coupé, P., Zhan, Y., Munsell, B., Rueckert, D. (eds) Patch-Based Techniques in Medical Imaging. Patch-MI 2015. Lecture Notes in Computer Science(), vol 9467. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28193-3

  • Online ISBN: 978-3-319-28194-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics