Nonlinear Image Representation Using Divisive Normalization
Abstract
In this paper, we describe a nonlinear image representation based on divisive normalization that is designed to match the statistical properties of photographic images, as well as the perceptual sensitivity of biological visual systems. We decompose an image using a multi-scale oriented representation, and use Student's t as a model of the dependencies within local clusters of coefficients. We then show that normalization of each coefficient by the square root of a linear combination of the amplitudes of the coefficients in the cluster reduces statistical dependencies. We further show that the resulting divisive normalization transform is invertible and provide an efficient iterative inversion algorithm. Finally, we probe the statistical and perceptual advantages of this image representation by examining its robustness to added noise, and using it to enhance image contrast.
Cite
Text
Lyu and Simoncelli. "Nonlinear Image Representation Using Divisive Normalization." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008. doi:10.1109/CVPR.2008.4587821Markdown
[Lyu and Simoncelli. "Nonlinear Image Representation Using Divisive Normalization." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008.](https://mlanthology.org/cvpr/2008/lyu2008cvpr-nonlinear/) doi:10.1109/CVPR.2008.4587821BibTeX
@inproceedings{lyu2008cvpr-nonlinear,
title = {{Nonlinear Image Representation Using Divisive Normalization}},
author = {Lyu, Siwei and Simoncelli, Eero P.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2008},
doi = {10.1109/CVPR.2008.4587821},
url = {https://mlanthology.org/cvpr/2008/lyu2008cvpr-nonlinear/}
}