An algorithm for super-resolution restoration of human faces based on self-example learning

Xiaoguang Li, Lansun Shen, Kin Man Lam, Suyu Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review


The paper proposes a novel super-resolution reconstruction algorithm for human faces. The algorithm extracts training examples from the input image and divides them into several classes using vector quantization. Then, it classifies each patch from a low-resolution image as one of these classes. Each class has its high-frequency information inferred using a parallel designed multi-class predictor, which is trained using the training samples from the same class. The self-example training set and the specific domain training set were employed in investigation of the impact of the training database. The experimental results showed the superior performance of the proposed method in terms of both the reconstruction quality and runtime.
Original languageEnglish
Pages (from-to)377-381
Number of pages5
JournalGaojishu Tongxin/Chinese High Technology Letters
Issue number4
Publication statusPublished - 1 Apr 2009


  • Example-based learning
  • Human face magnification
  • Multi-class predictor
  • Self-example
  • Super-resolution restoration

ASJC Scopus subject areas

  • Engineering(all)

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