Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction
arxiv(2024)
摘要
Model-based methods play a key role in the reconstruction of compressed
sensing (CS) MRI. Finding an effective prior to describe the statistical
distribution of the image family of interest is crucial for model-based
methods. Plug-and-play (PnP) is a general framework that uses denoising
algorithms as the prior or regularizer. Recent work showed that PnP methods
with denoisers based on pretrained convolutional neural networks outperform
other classical regularizers in CS MRI reconstruction. However, the numerical
solvers for PnP can be slow for CS MRI reconstruction. This paper proposes a
preconditioned PnP (P^2nP) method to accelerate the convergence speed.
Moreover, we provide proofs of the fixed-point convergence of the P^2nP
iterates. Numerical experiments on CS MRI reconstruction with non-Cartesian
sampling trajectories illustrate the effectiveness and efficiency of the P^2nP
approach.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要