A Two-Step Approach to Hallucinating Faces: Global Parametric Model and Local Nonparametric Model
Abstract
In this paper, we study face hallucination, or synthesizing a high-resolution face image from low-resolution input, with the help of a large collection of high-resolution face images. We develop a two-step statistical modeling approach that integrates both a global parametric model and a local nonparametric model. First, we derive a global linear model to learn the relationship between the high-resolution face images and their smoothed and down-sampled lower resolution ones. Second, the residual between an original high-resolution image and the reconstructed high-resolution image by a learned linear model is modeled by a patch-based nonparametric Markov network, to capture the high-frequency content of faces. By integrating both global and local models, we can generate photorealistic face images. Our approach is demonstrated by extensive experiments with high-quality hallucinated faces.
Cite
Text
Liu et al. "A Two-Step Approach to Hallucinating Faces: Global Parametric Model and Local Nonparametric Model." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001. doi:10.1109/CVPR.2001.990475Markdown
[Liu et al. "A Two-Step Approach to Hallucinating Faces: Global Parametric Model and Local Nonparametric Model." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001.](https://mlanthology.org/cvpr/2001/liu2001cvpr-two/) doi:10.1109/CVPR.2001.990475BibTeX
@inproceedings{liu2001cvpr-two,
title = {{A Two-Step Approach to Hallucinating Faces: Global Parametric Model and Local Nonparametric Model}},
author = {Liu, Ce and Shum, Heung-Yeung and Zhang, Changshui},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2001},
pages = {I:192-198},
doi = {10.1109/CVPR.2001.990475},
url = {https://mlanthology.org/cvpr/2001/liu2001cvpr-two/}
}