论文标题
D-VDAMP：基于Denoising的近似消息传递以抗压MRI
D-VDAMP: Denoising-based Approximate Message Passing for Compressive MRI
论文作者
论文摘要
插件（P＆P）算法迭代应用高度优化的图像DeNoiser，以强加先验并解决计算图像重建问题，从而极大地效果。但是，通常，在P＆P算法的迭代中，“有效噪声”是真实信号和中间解之间的差异，既不是高斯也不是白色。这一事实使现有的DeNo算法次优。 在这项工作中，我们提出了一个CNN结构，用于消除彩色高斯噪声，并将其与最近提出的VDAMP算法结合使用，该算法的有效噪声遵循可预测的彩色高斯分布。我们将所得的基于Denoisis的VDAMP（D-VDAMP）算法应用于可变密度采样的抗压MRI，在该密度中，它基本上胜过现有技术。
Plug and play (P&P) algorithms iteratively apply highly optimized image denoisers to impose priors and solve computational image reconstruction problems, to great effect. However, in general the "effective noise", that is the difference between the true signal and the intermediate solution, within the iterations of P&P algorithms is neither Gaussian nor white. This fact makes existing denoising algorithms suboptimal. In this work, we propose a CNN architecture for removing colored Gaussian noise and combine it with the recently proposed VDAMP algorithm, whose effective noise follows a predictable colored Gaussian distribution. We apply the resulting denoising-based VDAMP (D-VDAMP) algorithm to variable density sampled compressive MRI where it substantially outperforms existing techniques.
