Model Adaptation for Inverse Problems in Imaging

Deep neural networks have been applied successfully to a wide variety of inverse problems arising in computational imaging. These networks are typically trained using a forward model that describes the measurement process to be inverted, which is often incorporated directly into the network itself. However, these approaches lack robustness to drift of the forward model: if at test time the forward model varies (even slightly) from the one the network was trained for, the reconstruction performance can degrade substantially. Given a network trained to solve an initial inverse problem with a known forward model, we propose two novel procedures that adapt the network to a perturbed forward model, even without full knowledge of the perturbation. Our approaches do not require access to more labeled data (i.e., ground truth images), but only a small set of calibration measurements. We show these simple model adaptation procedures empirically achieve robustness to changes in the forward model in a variety of settings, including deblurring, super-resolution, and undersampled image reconstruction in magnetic resonance imaging.