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Directional total generalized variation regularization

  • Kongskov, Rasmus Dalgas
  • Dong, Yiqiu
  • Knudsen, Kim
Publication Date
Jan 01, 2019
Online Research Database In Technology
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In inverse problems, prior information and a priori-based regularization techniques play important roles. In this paper, we focus on image restoration problems, especially on restoring images whose texture mainly follow one direction. In order to incorporate the directional information, we propose a new directional total generalized variation (DTGV) functional, which is based on total generalized variation (TGV) by Bredies et al. After studying the mathematical properties of DTGV, we utilize it as regularizer and propose the L<sup>2</sup>-DTGV variational model for solving image restoration problems. Due to the requirement of the directional information in DTGV, we give a direction estimation algorithm, and then apply a primal-dual algorithm to solve the minimization problem. Experimental results show the effectiveness of the proposed method for restoring the directional images. In comparison with isotropic regularizers like total variation and TGV, the improvement of texture preservation and noise removal is significant.

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