  We introduce a model based off-the-grid image reconstruction algorithm using
deep learned priors. The main difference of the proposed scheme with current
deep learning strategies is the learning of non-linear annihilation relations
in Fourier space. We rely on a model based framework, which allows us to use a
significantly smaller deep network, compared to direct approaches that also
learn how to invert the forward model. Preliminary comparisons against image
domain MoDL approach demonstrates the potential of the off-the-grid
formulation. The main benefit of the proposed scheme compared to structured
low-rank methods is the quite significant reduction in computational
complexity.
