Hallucinating face by position-patch

  • Authors:
  • Xiang Ma;Junping Zhang;Chun Qi

  • Affiliations:
  • School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China;Department of Computer Science and Engineering, Fudan University, Shanghai 200433, China;School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China

  • Venue:
  • Pattern Recognition
  • Year:
  • 2010

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Abstract

A novel face hallucination method is proposed in this paper for the reconstruction of a high-resolution face image from a low-resolution observation based on a set of high- and low-resolution training image pairs. Different from most of the established methods based on probabilistic or manifold learning models, the proposed method hallucinates the high-resolution image patch using the same position image patches of each training image. The optimal weights of the training image position-patches are estimated and the hallucinated patches are reconstructed using the same weights. The final high-resolution facial image is formed by integrating the hallucinated patches. The necessity of two-step framework or residue compensation and the differences between hallucination based on patch and global image are discussed. Experiments show that the proposed method without residue compensation generates higher-quality images and costs less computational time than some recent face image super-resolution (hallucination) techniques.