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• W3209767343 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." @default.
• W3209767343 created "2021-11-08" @default.
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• W3209767343 date "2022-01-01" @default.
• W3209767343 modified "2023-10-17" @default.
• W3209767343 title "Compressed Sensing MRI by Integrating Deep Denoiser and Weighted Schatten P-Norm Minimization" @default.
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• W3209767343 doi "https://doi.org/10.1109/lsp.2021.3122338" @default.
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