Plug-and-play (PnP) methods have recently emerged as a powerful
framework for image reconstruction that can flexibly combine different
physics-based observation models with data-driven image priors
in the form of denoisers, and achieve state-of-the-art image reconstruction
quality in many applications. In this paper, we aim to further
improve the computational efficacy of PnP methods by designing
a new algorithm that makes use of stochastic variance-reduced
gradients (SVRG), a nascent idea to accelerate runtime in stochastic
optimization. Compared with existing PnP methods using batch gradients
or stochastic gradients, the new algorithm, called PnP-SVRG,
achieves comparable or better accuracy of image reconstruction at a
much faster computational speed. Extensive numerical experiments
are provided to demonstrate the benefits of the proposed algorithm
through the application of compressive imaging using partial Fourier
measurements in conjunction with a wide variety of popular image
denoisers.