Plug-and-play (PnP) is a non-convex framework that combines ADMM or other
proximal algorithms with advanced denoiser priors. Recently, PnP has achieved
great empirical success, especially with the integration of deep learning-based
denoisers. However, a key problem of PnP based approaches is that they require
manual parameter tweaking. It is necessary to obtain high-quality results
across the high discrepancy in terms of imaging conditions and varying scene
content. In this work, we present a tuning-free PnP proximal algorithm, which
can automatically determine the internal parameters including the penalty
parameter, the denoising strength and the terminal time. A key part of our
approach is to develop a policy network for automatic search of parameters,
which can be effectively learned via mixed model-free and model-based deep
reinforcement learning. We demonstrate, through numerical and visual
experiments, that the learned policy can customize different parameters for
different states, and often more efficient and effective than existing
handcrafted criteria. Moreover, we discuss the practical considerations of the
plugged denoisers, which together with our learned policy yield
state-of-the-art results. This is prevalent on both linear and nonlinear
exemplary inverse imaging problems, and in particular, we show promising
results on Compressed Sensing MRI and phase retrieval.

本文提出了一种可实现自动调参的 PnP 近端算法，利用混合模型自由和基于模型的深度强化学习方法，开发出策略网络进行参数自动搜索，从而得到最优参数值，可在重构图像方面提供 state-of-the-art 的效果，尤其在压缩感知 MRI 和相位恢复上结果有很大提升。