Authors:
Siavash Arjomand Bigdeli
1
and
Matthias Zwicker
2
Affiliations:
1
University of Bern, Switzerland
;
2
University of Bern and University of Maryland, Switzerland
Keyword(s):
Image Restoration, Denoising Autoencoders, Mean Shift.
Related
Ontology
Subjects/Areas/Topics:
Computational Photography
;
Computer Vision, Visualization and Computer Graphics
;
Image Enhancement and Restoration
;
Image Formation and Preprocessing
;
Rendering
Abstract:
We propose to leverage denoising autoencoder networks as priors to address image restoration problems. We
build on the key observation that the output of an optimal denoising autoencoder is a local mean of the true
data density, and the autoencoder error (the difference between the output and input of the trained autoencoder)
is a mean shift vector. We use the magnitude of this mean shift vector, that is, the distance to the local mean,
as the negative log likelihood of our natural image prior. For image restoration, we maximize the likelihood
using gradient descent by backpropagating the autoencoder error. A key advantage of our approach is that we
do not need to train separate networks for different image restoration tasks, such as non-blind deconvolution
with different kernels, or super-resolution at different magnification factors. We demonstrate state of the art
results for non-blind deconvolution and super-resolution using the same autoencoding prior.