NeurIPS 2017spotlight156 citations
Deep Mean-Shift Priors for Image Restoration
Siavash Arjomand Bigdeli, Matthias Zwicker, Paolo Favaro, Meiguang Jin
Abstract
In this paper we introduce a natural image prior that directly represents a Gaussian-smoothed version of the natural image distribution. We include our prior in a formulation of image restoration as a Bayes estimator that also allows us to solve noise-blind image restoration problems. We show that the gradient of our prior corresponds to the mean-shift vector on the natural image distribution. In addition, we learn the mean-shift vector field using denoising autoencoders, and use it in a gradient descent approach to perform Bayes risk minimization. We demonstrate competitive results for noise-blind deblurring, super-resolution, and demosaicing.
BibTeX
@inproceedings{NIPS2017_38913e1d,
author = {Arjomand Bigdeli, Siavash and Zwicker, Matthias and Favaro, Paolo and Jin, Meiguang},
booktitle = {Advances in Neural Information Processing Systems},
editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Deep Mean-Shift Priors for Image Restoration},
url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/38913e1d6a7b94cb0f55994f679f5956-Paper.pdf},
volume = {30},
year = {2017}
}