Face hallucination via sparse coding

Jianchao Yang, Hao Tang, Yi Ma, Thomas Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we address the problem of hallucinating a high resolution face given a low resolution input face. The problem is approached through sparse coding. To exploit the facial structure, Non-negative Matrix Factorization (NMF) [1] is first employed to learn a localized part-based subspace. This subspace is effective for super-resolving the incoming low resolution face under reconstruction constraints. To further enhance the detailed facial information, we propose a local patch method based on sparse representation with respect to coupled overcomplete patch dictionaries, which can be fast solved through linear programming. Experiments demonstrate that our approach can hallucinate high quality super-resolution faces.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings
Number of pages4
StatePublished - 2008
Event2008 IEEE International Conference on Image Processing, ICIP 2008 - San Diego, CA, United States
Duration: Oct 12 2008Oct 15 2008

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Other2008 IEEE International Conference on Image Processing, ICIP 2008
Country/TerritoryUnited States
CitySan Diego, CA


  • Face hallucination
  • Nonnegative matrix factorization
  • Sparse coding
  • Sparse representation
  • Super-resolution

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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