SGTD: Structure Gradient and Texture Decorrelating Regularization for Image Decomposition

Qiegen Liu, Jianbo Liu, Pei Dong, Dong Liang

ICCV 2013

doi:10.1109/ICCV.2013.138 /iccv/2013/liu2013iccv-sgtd/

Abstract

This paper presents a novel structure gradient and texture decorrelating regularization (SGTD) for image decomposition. The motivation of the idea is under the assumption that the structure gradient and texture components should be properly decorrelated for a successful decomposition. The proposed model consists of the data fidelity term, total variation regularization and the SGTD regularization. An augmented Lagrangian method is proposed to address this optimization issue, by first transforming the unconstrained problem to an equivalent constrained problem and then applying an alternating direction method to iteratively solve the subproblems. Experimental results demonstrate that the proposed method presents better or comparable performance as state-of-the-art methods do.

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Cite

Text

Liu et al. "SGTD: Structure Gradient and Texture Decorrelating Regularization for Image Decomposition." International Conference on Computer Vision, 2013. doi:10.1109/ICCV.2013.138

Markdown

[Liu et al. "SGTD: Structure Gradient and Texture Decorrelating Regularization for Image Decomposition." International Conference on Computer Vision, 2013.](https://mlanthology.org/iccv/2013/liu2013iccv-sgtd/) doi:10.1109/ICCV.2013.138

BibTeX

@inproceedings{liu2013iccv-sgtd,
  title     = {{SGTD: Structure Gradient and Texture Decorrelating Regularization for Image Decomposition}},
  author    = {Liu, Qiegen and Liu, Jianbo and Dong, Pei and Liang, Dong},
  booktitle = {International Conference on Computer Vision},
  year      = {2013},
  doi       = {10.1109/ICCV.2013.138},
  url       = {https://mlanthology.org/iccv/2013/liu2013iccv-sgtd/}
}