  REgularization by Denoising (RED) is an attractive framework for solving
inverse problems by incorporating state-of-the-art denoising algorithms as the
priors. A drawback of this approach is the high computational complexity of
denoisers, which dominate the computation time. In this paper, we apply a
general framework called weighted proximal methods (WPMs) to solve RED
efficiently. We first show that two recently introduced RED solvers (using the
fixed point and accelerated proximal gradient methods) are particular cases of
WPMs. Then we show by numerical experiments that slightly more sophisticated
variants of WPM can lead to reduced run times for RED by requiring a
significantly smaller number of calls to the denoiser.
