Abstract
Models play an important role in inverse problems, serving as the prior for representing the original signal to be recovered. REgularization by Denoising (RED) is a recently introduced general framework for constructing such priors using state-of-the-art denoising algorithms. Using RED, solving inverse problems is shown to amount to an iterated denoising process. However, as the complexity of denoising algorithms is generally high, this might lead to an overall slow algorithm. In this paper, we suggest an accelerated technique based on vector extrapolation (VE) to speed-up existing RED solvers. Numerical experiments validate the obtained gain by VE, leading to a substantial savings in computations compared with the original fixed-point method.
Abstract (translated)
模型在反问题中起着重要的作用，作为表示待恢复原始信号的先验条件。规则化去噪（RED）是最近引入的一个通用框架，用于使用最先进的去噪算法构造此类先验。使用RED，反问题的求解被证明是一个迭代去噪过程。然而，由于去噪算法的复杂度通常很高，这可能导致一个整体的缓慢算法。本文提出了一种基于矢量外推法（ve）的加速技术来加速现有的红色解算器。数值实验验证了文中所得到的增益，与原定点法相比，大大节省了计算量。
URL
https://arxiv.org/abs/1805.02158
