Learning local pixel structure for face hallucination

Yu Hu, Kin Man Lam, Guoping Qiu, Tingzhi Shen, Hui Tian

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)


In this paper, we present a novel learning-based face hallucination method based on the assumption that similar faces will have similar local pixel structures. We use the low- resolution (LR) input face to search a database for K example faces that are the most similar to the input and align them with the input accordingly. The local pixel structures of the target high-resolution (HR) image are learned from those warped HR example faces in a neighbor embedding manner, and a total variation (TV) constraint is employed to aid the learning of all pixels'embedding weights. The learned local pixel structures are then used as constraints to reconstruct a HR version of the input face. Experimental results show that the method performs well in terms of both reconstruction error and visual quality.
Original languageEnglish
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Number of pages4
Publication statusPublished - 1 Dec 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: 26 Sept 201029 Sept 2010


Conference2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/TerritoryHong Kong
CityHong Kong


  • Face hallucination
  • Local pixel structure
  • Super resolution
  • TV norm

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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