IMAGE DENOISING WITH DEEP UNFOLDING AND NORMALIZING FLOWS

Many application domains, spanning from low-level computer vision to medical imaging, require high-fidelity images from noisy measurements. State of the art methods for solving denoising problems combine deep learning with iterative model-based solvers, a concept known as deep algorithm unfolding or unrolling. By combining a-priori knowledge of the forward measurement model with learned proximal image-to-image mappings based on deep networks, these methods yield solutions that are both physically feasible (data-consistent) and perceptually plausible (consistent with prior belief). However, current proximal mappings based on (predominantly convolutional) neural networks 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, and training the entire algorithm in an end-to-end fashion. We demonstrate that the proposed method outperforms competitive baselines on image denoising.