Multi-frame super-resolution with quality self-assessment for retinal fundus videos

Med Image Comput Comput Assist Interv. 2014;17(Pt 1):650-7. doi: 10.1007/978-3-319-10404-1_81.


This paper proposes a novel super-resolution framework to reconstruct high-resolution fundus images from multiple low-resolution video frames in retinal fundus imaging. Natural eye movements during an examination are used as a cue for super-resolution in a robust maximum a-posteriori scheme. In order to compensate heterogeneous illumination on the fundus, we integrate retrospective illumination correction for photometric registration to the underlying imaging model. Our method utilizes quality self-assessment to provide objective quality scores for reconstructed images as well as to select regularization parameters automatically. In our evaluation on real data acquired from six human subjects with a low-cost video camera, the proposed method achieved considerable enhancements of low-resolution frames and improved noise and sharpness characteristics by 74%. In terms of image analysis, we demonstrate the importance of our method for the improvement of automatic blood vessel segmentation as an example application, where the sensitivity was increased by 13% using super-resolution reconstruction.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Feedback
  • Fundus Oculi
  • Humans
  • Image Enhancement / methods*
  • Image Interpretation, Computer-Assisted / methods*
  • Reproducibility of Results
  • Retinal Vessels / anatomy & histology*
  • Retinoscopy / methods*
  • Sensitivity and Specificity
  • Subtraction Technique*
  • Video Recording / methods*