  Machine learning models are commonly trained end-to-end and in a supervised
setting, using paired (input, output) data. Examples include recent
super-resolution methods that train on pairs of (low-resolution,
high-resolution) images. However, these end-to-end approaches require
re-training every time there is a distribution shift in the inputs (e.g., night
images vs daylight) or relevant latent variables (e.g., camera blur or hand
motion). In this work, we leverage state-of-the-art (SOTA) generative models
(here StyleGAN2) for building powerful image priors, which enable application
of Bayes' theorem for many downstream reconstruction tasks. Our method,
Bayesian Reconstruction through Generative Models (BRGM), uses a single
pre-trained generator model to solve different image restoration tasks, i.e.,
super-resolution and in-painting, by combining it with different forward
corruption models. We keep the weights of the generator model fixed, and
reconstruct the image by estimating the Bayesian maximum a-posteriori (MAP)
estimate over the input latent vector that generated the reconstructed image.
We further use variational inference to approximate the posterior distribution
over the latent vectors, from which we sample multiple solutions. We
demonstrate BRGM on three large and diverse datasets: (i) 60,000 images from
the Flick Faces High Quality dataset (ii) 240,000 chest X-rays from MIMIC III
and (iii) a combined collection of 5 brain MRI datasets with 7,329 scans.
Across all three datasets and without any dataset-specific hyperparameter
tuning, our simple approach yields performance competitive with current
task-specific state-of-the-art methods on super-resolution and in-painting,
while being more generalisable and without requiring any training. Our source
code and pre-trained models are available online:
https://razvanmarinescu.github.io/brgm/.
