  Many application domains, spanning from computational photography to medical
imaging, require recovery of high-fidelity images from noisy, incomplete or
partial/compressed measurements. State of the art methods for solving these
inverse problems combine deep learning with iterative model-based solvers, a
concept known as deep algorithm unfolding. By combining a-priori knowledge of
the forward measurement model with learned (proximal) mappings based on deep
networks, these methods yield solutions that are both physically feasible
(data-consistent) and perceptually plausible. However, current proximal
mappings only implicitly learn such image priors. In this paper, we propose to
make these image priors fully explicit by embedding deep generative models in
the form of normalizing flows within the unfolded proximal gradient algorithm.
We demonstrate that the proposed method outperforms competitive baselines on
various image recovery tasks, spanning from image denoising to inpainting and
deblurring.
