  This paper presents a deep neural network called DIVA unfolding a baseline
adaptive denoising algorithm (De-QuIP), relying on the theory of quantum
many-body physics. Furthermore, it is shown that with very slight
modifications, this network can be enhanced to solve more challenging image
restoration tasks such as image deblurring, super-resolution and inpainting.
Despite a compact and interpretable (from a physical perspective) architecture,
the proposed deep learning network outperforms several recent algorithms from
the literature, designed specifically for each task. The key ingredients of the
proposed method are on one hand, its ability to handle non-local image
structures through the patch-interaction term and the quantum-based Hamiltonian
operator, and, on the other hand, its flexibility to adapt the hyperparameters
patch-wisely, due to the training process.
