Compressed Sensing MRI by Integrating Deep Denoiser and Weighted Schatten P-Norm Minimization

Abstract

To efficiently reconstruct magnetic resonance images (MRI) from highly undersampled measurements by using compressed sensing (CS), in this letter, we propose a hybrid regularization model from deep prior and low-rank prior. The local deep prior is explored by a fast flexible denoising convolutional neural network (FFDNet). To compensate for 1) the generalization capability of FFDNet on artifact noise caused by undersampling K-space and 2) the inaccurate noise estimation for various undersampling ratios, we model the low-rank prior as a weighted Schatten p-norm to obtain the global information of MRIs. The final model, combined by the local deep and low-rank priors, is solved by the alternating directional method of multipliers under the plug-and-play framework. Compared with the popular CS-MRI approaches, the experimental results demonstrate that the proposed method can achieve better reconstruction performance in terms of quality index and visual effects.