MRI的即插即用自整定算法
Autotuning Plug-and-Play Algorithms for MRI
摘要：
对于磁共振成像（MRI），最近提出的“即插即用”（PIMs）图像恢复算法已显示出显着的性能。这些PSNR算法类似于传统的迭代算法，如FISTA、ADMM或原始-对偶分裂（PDS），但不同之处在于最近更新被调用特定于应用的图像去噪器（如BM 3D或DnCNN）所取代。Pestrian算法的固定点取决于算法步长参数，然而，必须调整以获得最佳性能。在这项工作中，我们提出了一个快速和强大的自动调整PnP-PDS算法，利用知识的测量噪声方差，可从MRI中的预扫描。实验结果表明，我们的算法收敛非常接近基因调整的性能，这样做明显快于现有的自动调整方法。
英文摘要：
For magnetic resonance imaging (MRI), recently proposed "plug-and-play" (PnP) image recovery algorithms have shown remarkable performance. These PnP algorithms are similar to traditional iterative algorithms like FISTA, ADMM, or primal-dual splitting (PDS), but differ in that the proximal update is replaced by a call to an application-specific image denoiser, such as BM3D or DnCNN. The fixed-points of PnP algorithms depend upon an algorithmic stepsize parameter, however, which must be tuned for optimal performance. In this work, we propose a fast and robust auto-tuning PnP-PDS algorithm that exploits knowledge of the measurement-noise variance that is available from a pre-scan in MRI. Experimental results show that our algorithm converges very close to genie-tuned performance, and does so significantly faster than existing autotuning approaches.
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