Image Restoration using Autoencoding Priors

Understand

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.