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Contextual-Field Supported Iterative Representation for Face Hallucination

  • Kangli Zeng
  • Tao Lu
  • Xiaolin Li
  • Yanduo Zhang
  • Li Peng
  • Shenming Qu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11336)

Abstract

Face hallucination is a special super-resolution (SR) algorithm that enhances the resolution and quality of low-resolution (LR) facial image. For reconstructing finer high frequency information which are missing in image degradation, learning-based face SR methods rely on accurate prior information from training samples. In this paper, we propose a contextual-field supported iterative representation algorithm for face hallucination to discovery accurate prior. Different from traditional local-patch based methods, we use contextual-field supported sampling to replace local receptive field patch sampling for enriching prior information. Then, two weighted matrices are introduced to constrain reconstruction-errors term and representation-coefficients term simultaneously, one matrix ameliorates the heteroscedasticity of real data and the other one improves the stability of solution. Finally, we use iterative representation learning to iteratively update the supported dictionary pairs and their representation-coefficients to refine accurate high-frequency information. The experimental results show that the proposed approach outperforms some state-of-the-art face hallucination methods over FERET and CMU-MIT face databases using both subjective and objective evaluation indexes.

Keywords

Face hallucination Iterative representation Contextual information Dictionary learning 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Hubei Key Laboratory of Intelligent Robot, School of Computer Science and EngineeringWuhan Institute of TechnologyWuhanChina
  2. 2.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
  3. 3.School of SoftwareHe’nan UniversityKaifengChina

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