  High resolution Magnetic Resonance (MR) images are desired for accurate
diagnostics. In practice, image resolution is restricted by factors like
hardware and processing constraints. Recently, deep learning methods have been
shown to produce compelling state-of-the-art results for image
enhancement/super-resolution. Paying particular attention to desired
hi-resolution MR image structure, we propose a new regularized network that
exploits image priors, namely a low-rank structure and a sharpness prior to
enhance deep MR image super-resolution (SR). Our contributions are then
incorporating these priors in an analytically tractable fashion \color{black}
as well as towards a novel prior guided network architecture that accomplishes
the super-resolution task. This is particularly challenging for the low rank
prior since the rank is not a differentiable function of the image matrix(and
hence the network parameters), an issue we address by pursuing differentiable
approximations of the rank. Sharpness is emphasized by the variance of the
Laplacian which we show can be implemented by a fixed feedback layer at the
output of the network. As a key extension, we modify the fixed feedback
(Laplacian) layer by learning a new set of training data driven filters that
are optimized for enhanced sharpness. Experiments performed on publicly
available MR brain image databases and comparisons against existing
state-of-the-art methods show that the proposed prior guided network offers
significant practical gains in terms of improved SNR/image quality measures.
Because our priors are on output images, the proposed method is versatile and
can be combined with a wide variety of existing network architectures to
further enhance their performance.
