Plug-and-Play Methods Provably Converge with Properly Trained Denoisers

14 May 2019Ernest K. RyuJialin LiuSicheng WangXiaohan ChenZhangyang WangWotao Yin

Plug-and-play (PnP) is a non-convex framework that integrates modern denoising priors, such as BM3D or deep learning-based denoisers, into ADMM or other proximal algorithms. An advantage of PnP is that one can use pre-trained denoisers when there is not sufficient data for end-to-end training... (read more)

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