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Acceleration of RED via vector extrapolation

机译:通过向量外推加速RED

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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 substantial savings in computations compared with the original fixed-point method. (C) 2019 Elsevier Inc. All rights reserved.
机译:模型在逆问题中起着重要作用,是表示要恢复的原始信号的先验条件。通过降噪进行归一化(RED)是一种最新引入的通用框架,用于使用最新的去噪算法来构造此类先验。使用RED,解决逆问题显示为迭代的去噪过程。但是,由于去噪算法的复杂度通常很高,这可能会导致整体算法变慢。在本文中,我们建议一种基于矢量外推(VE)的加速技术,以加快现有RED求解器的速度。数值实验验证了通过VE获得的增益,与原始定点方法相比,可节省大量计算。 (C)2019 Elsevier Inc.保留所有权利。

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