ML Anthology

A Variable Exponent P-Laplace Variational Model Preserving Texture for Image Interpolation

Zhan Yi, Yongxin Ge
WACVW 2017 pp. 36-41
doi:10.1109/WACVW.2017.13 /wacvw/2017/yi2017wacvw-variable/

Abstract

In this paper, we propose a modified variable exponent p-Laplace energy functional for image interpolation. This model enhances the strength of isotropic diffusion at homogeneous areas and retains the anisotropic diffusion at image edges, which diminishes the width of edges of interpolated image thereby obtaining crisp image contours. The two parameters control the strength of isotropic and anisotropic diffusion, and determine the degree to which we want to preserve the fine texture of image, respectively. Numerical experiments on real images show that images interpolated by the proposed method have better interpolated edges, especially for fine textures.

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Cite

Text

Yi and Ge. "A Variable Exponent P-Laplace Variational Model Preserving Texture for Image Interpolation." IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, 2017. doi:10.1109/WACVW.2017.13

Markdown

[Yi and Ge. "A Variable Exponent P-Laplace Variational Model Preserving Texture for Image Interpolation." IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, 2017.](https://mlanthology.org/wacvw/2017/yi2017wacvw-variable/) doi:10.1109/WACVW.2017.13

BibTeX

@inproceedings{yi2017wacvw-variable,
  title     = {{A Variable Exponent P-Laplace Variational Model Preserving Texture for Image Interpolation}},
  author    = {Yi, Zhan and Ge, Yongxin},
  booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision Workshops},
  year      = {2017},
  pages     = {36-41},
  doi       = {10.1109/WACVW.2017.13},
  url       = {https://mlanthology.org/wacvw/2017/yi2017wacvw-variable/}
}