LRTV: MR Image Super-Resolution with Low-Rank and Total Variation Regularizations

Feng Shi, Jian Cheng, Li Wang, Pew Thian Yap, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

170 Citations (Scopus)


Image super-resolution (SR) aims to recover high-resolution images from their low-resolution counterparts for improving image analysis and visualization. Interpolation methods, widely used for this purpose, often result in images with blurred edges and blocking effects. More advanced methods such as total variation (TV) retain edge sharpness during image recovery. However, these methods only utilize information from local neighborhoods, neglecting useful information from remote voxels. In this paper, we propose a novel image SR method that integrates both local and global information for effective image recovery. This is achieved by, in addition to TV, low-rank regularization that enables utilization of information throughout the image. The optimization problem can be solved effectively via alternating direction method of multipliers (ADMM). Experiments on MR images of both adult and pediatric subjects demonstrate that the proposed method enhances the details in the recovered high-resolution images, and outperforms methods such as the nearest-neighbor interpolation, cubic interpolation, iterative back projection (IBP), non-local means (NLM), and TV-based up-sampling.

Original languageEnglish
Article number7113897
Pages (from-to)2459-2466
Number of pages8
JournalIEEE Transactions on Medical Imaging
Issue number12
Publication statusPublished - 2015 Dec


  • Image enhancement
  • image sampling
  • matrix completion
  • sparse learning
  • spatial resolution

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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