Abstract
Deep convolutional networks have become a popular
tool for image generation and restoration. Generally, their
excellent performance is imputed to their ability to learn realistic image priors from a large number of example images.
In this paper, we show that, on the contrary, the structure of
a generator network is sufficient to capture a great deal of
low-level image statistics prior to any learning. In order
to do so, we show that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, superresolution, and inpainting. Furthermore, the same prior
can be used to invert deep neural representations to diagnose them, and to restore images based on flash-no flash
input pairs.
Apart from its diverse applications, our approach highlights the inductive bias captured by standard generator
network architectures. It also bridges the gap between
two very popular families of image restoration methods:
learning-based methods using deep convolutional networks
and learning-free methods based on handcrafted image priors such as self-similarity