Off-the-grid model based deep learning (O-MODL)

The popular approach is to constrain the reconstructions using compactness priors including sparsity. Several researchers have recently introduced off-the-grid continuous domain priors that are robust to discretization errors [1, 2], which provide significantly improved image quality in a range of applications. However, the main challenge is the significant increase in computational complexity. Recently, several researchers have introduced deep learning methodsas fast and efficient alternatives to compressed sensing algorithms. Current approaches can be categorized into direct and model based strategies. The direct approaches directly estimate the images from the undersampled measurements ortheir transforms/features [3, 4]. These methods learn to invert the forward operator over the space/manifold of images. Whilethis approach is more popular, a challenge with these schemes is the need to learn the inverse, which often requires large models (e.g.