  Plug-and-play priors (PnP) is a broadly applicable methodology for solving
inverse problems by exploiting statistical priors specified as denoisers.
Recent work has reported the state-of-the-art performance of PnP algorithms
using pre-trained deep neural nets as denoisers in a number of imaging
applications. However, current PnP algorithms are impractical in large-scale
settings due to their heavy computational and memory requirements. This work
addresses this issue by proposing an incremental variant of the widely used
PnP-ADMM algorithm, making it scalable to large-scale datasets. We
theoretically analyze the convergence of the algorithm under a set of explicit
assumptions, extending recent theoretical results in the area. Additionally, we
show the effectiveness of our algorithm with nonsmooth data-fidelity terms and
deep neural net priors, its fast convergence compared to existing PnP
algorithms, and its scalability in terms of speed and memory.
