title: Super-resolution for Medical ImageviaSparse Representation and Adaptive M-estimator.
abstract: OBJECTIVE
  The goal of super-resolution is to generate high-resolution images from low-resolution input images.
  
  
  METHODS
  In this paper, a combined method based on sparse signal representation and adaptive M-estimator is proposed for single-image super-resolution. With the sparse signal representation, the correlation between the sparse representation of high-resolution patches and that of low-resolution patches for the identical image is learned as a set of joint dictionaries and a set of high-resolution patches is obtained for high- and low-resolution patches. Then the dictionaries and high-resolution patches are used to produce the high-resolution image for a low-resolution single image.
  
  
  RESULTS
  At the post-processing phase, the adaptive M-estimator, combining the advantages of traditional L1and L2norms, is used to give further processing for the resultant high-resolution image, to reduce the artefact by learning and reconstitution, and improve the performance.
  
  
  CONCLUSIONS
  Three experimental results show the performance improvement of the proposed algorithm over other methods.
authors.affiliation: Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan 430000, China.|College of Biomedical Engineering, South Central University of Nationalities, Wuhan 430000, China, e-mail: qinlanxie@126.com.
authors.forename: Q
authors.initials: Q
authors.lastname: Xie
authors.affiliation: Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan 430000, China.
authors.forename: N
authors.initials: N
authors.lastname: Sang
keywords: Adaptive M-estimator; image dictionary; image super-resolution; learning-based method; sparse representation