  Regularization by denoising (RED) is a recently developed framework for
solving inverse problems by integrating advanced denoisers as image priors.
Recent work has shown its state-of-the-art performance when combined with
pre-trained deep denoisers. However, current RED algorithms are inadequate for
parallel processing on multicore systems. We address this issue by proposing a
new asynchronous RED (ASYNC-RED) algorithm that enables asynchronous parallel
processing of data, making it significantly faster than its serial counterparts
for large-scale inverse problems. The computational complexity of ASYNC-RED is
further reduced by using a random subset of measurements at every iteration. We
present complete theoretical analysis of the algorithm by establishing its
convergence under explicit assumptions on the data-fidelity and the denoiser.
We validate ASYNC-RED on image recovery using pre-trained deep denoisers as
priors.
