Multiframe super-resolution reconstruction using sparse directional regularization

Yan Ran Li, Dao Qing Dai, Lixin Shen

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


We present a variational approach to obtain high-resolution images from multiframe low-resolution video stills. The objective functional for the variational approach consists of a data fidelity term and a regularizer. The fidelity term is formed by adaptively mimicking ell and ell norms. The regularization uses the ell norm of the framelet coefficients of a high-resolution image with a geometric tight framelet system constructed in this paper. The tight framelet system has abilities to detect multi-orientation and multi-order variations of an image. A two-phase iterative method for super-resolution reconstruction is proposed to construct a high-resolution image. The first phase is to get an approximation of the solution (i.e., the ideal image) using the steepest descent method. The second phase is to enhance the sparsity of the approximate solution by using the soft thresholding operator with variable thresholding parameters. Numerical results based on both synthetic data and real videos show that our algorithm is efficient in terms of removing visual artifacts and preserving edges in restored images.

Original languageEnglish (US)
Article number5432964
Pages (from-to)945-956
Number of pages12
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number7
StatePublished - Jul 2010


  • < norm
  • Sparse directional regularization
  • Super-resolution
  • Tight framelet
  • Wavelet

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering


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