Sparse representation and position prior based face hallucination upon classified over-complete dictionaries

  • Authors:
  • Xiang Ma;Hiêp Quang Luong;Wilfried Philips;Huansheng Song;Hua Cui

  • Affiliations:
  • School of Information Engineering, Chang'an University, Xi'an 710064, China;Ghent University, TELIN-IPI-IBBT, Sint-Pietersnieuwstraat 41, B-9000 Ghent, Belgium;Ghent University, TELIN-IPI-IBBT, Sint-Pietersnieuwstraat 41, B-9000 Ghent, Belgium;School of Information Engineering, Chang'an University, Xi'an 710064, China;School of Information Engineering, Chang'an University, Xi'an 710064, China

  • Venue:
  • Signal Processing
  • Year:
  • 2012

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Abstract

In compressed sensing theory, decomposing a signal based upon redundant dictionaries is of considerable interest for data representation in signal processing. The signal is approximated by an over-complete dictionary instead of an orthonormal basis for adaptive sparse image decompositions. Existing sparsity-based super-resolution methods commonly train all atoms to construct only a single dictionary for super-resolution. However, this approach results in low precision of reconstruction. Furthermore, the process of generating such dictionary usually involves a huge computational cost. This paper proposes a sparse representation and position prior based face hallucination method for single face image super-resolution. The high- and low-resolution atoms for the first time are classified to form local dictionaries according to the different regions of human face, instead of generating a single global dictionary. Different local dictionaries are used to hallucinate the corresponding regions of face. The patches of the low-resolution face inputs are approximated respectively by a sparse linear combination of the atoms in the corresponding over-complete dictionaries. The sparse coefficients are then obtained to generate high-resolution data under the constraint of the position prior of face. Experimental results illustrate that the proposed method can hallucinate face images of higher quality with a lower computational cost compared to other existing methods.