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.
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Best student paper award!