Image Restoration using Autoencoding Priors

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... (read more)

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Denoising Autoencoder
Generative Models
Generative Models