An improved joint dictionary training method for single image super resolution

• Lei Zeng ^[1] ; Xiaofeng Li ^[1] ; Jin Xu ^[1]
  1. [1] University of Electronic Science and Technology of China

     University of Electronic Science and Technology of China

     China

     
• Localización: Compel: International journal for computation and mathematics in electrical and electronic engineering, ISSN 0332-1649, Vol. 32, Nº 2 (Special Issue: CAC 2010), 2013, págs. 721-727
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen

  • Purpose – The purpose of this paper is to introduce an improved method for joint training of low‐ and high‐resolution dictionaries in the single image super resolution. With simulations, the proposed method is thereafter evaluated.

    Design/methodology/approach – Sparse representations of low‐resolution image patches are used to reconstruct the high‐resolution image patches with high resolution dictionary. By using different factors, the scheme weights the two dictionaries in the high‐ and low‐resolution spaces in the training process. It is reasonable to achieve better reconstructed images with more emphasis on the high‐resolution spaces.

    Findings – An improved joint training algorithm based on K‐SVD is developed with flexible weight factors on dictionaries in the high‐ and low‐resolution spaces. From the experiment results, the proposed scheme outperforms the classic bicubic interpolation and neighbor‐embedding learning based method.

    Originality/value – By using flexible weight factors in joint training of the dictionaries for super resolution, better reconstruction results can be achieved.