Deep MR Image Super-Resolution Using Structural Priors

High resolution magnetic resonance (MR) images are desired for accurate diagnostics. In practice, image resolution is restricted by factors like hardware, cost and processing constraints. Recently, deep learning methods have been shown to produce compelling state of the art results for image 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 superresolution. Our contributions are then incorporating these priors in an analytically tractable fashion in the learning of a convolutional neural network (CNN) 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. Experiments performed on two publicly available MR brain image databases exhibit promising results particularly when training imagery is limited.


page 3

page 4


Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors

High resolution Magnetic Resonance (MR) images are desired for accurate ...

Structural Prior Driven Regularized Deep Learning for Sonar Image Classification

Deep learning has been recently shown to improve performance in the doma...

Improved Super Resolution of MR Images Using CNNs and Vision Transformers

State of the art magnetic resonance (MR) image super-resolution methods ...

How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution

Wisely utilizing the internal and external learning methods is a new cha...

Single MR Image Super-Resolution via Channel Splitting and Serial Fusion Network

Spatial resolution is a critical imaging parameter in magnetic resonance...

Super-resolution MRI Using Finite Rate of Innovation Curves

We propose a two-stage algorithm for the super-resolution of MR images f...

Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain

Magnetic resonance imaging plays an important role in computer-aided dia...

Please sign up or login with your details

Forgot password? Click here to reset