Acceleration of RED via Vector Extrapolation
This work addresses efficiency for practitioners using RED frameworks, but it is incremental as it builds on existing methods.
The paper tackles the computational slowness of RED solvers in inverse problems by proposing an acceleration technique using vector extrapolation, resulting in substantial computational savings validated through numerical experiments.
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.