Plug-and-Play (PnP) methods are a class of efficient iterative methods that aim to combine data fidelity terms and deep denoisers using classical optimization algorithms, such as ISTA or ADMM. Existing provable PnP methods impose heavy restrictions on the denoiser or fidelity function, such as nonexpansiveness or strict convexity. In this work, we propose a provable PnP method that imposes relatively light conditions based on proximal denoisers, and introduce a quasi-Newton step to greatly accelerate convergence. By specially parameterizing the deep denoiser as a gradient step, we further characterize the fixed-points of the quasi-Newton PnP algorithm.

翻译：Plug-Play (PnP) 方法是一种高效的迭代方法,目的是利用传统的优化算法,例如ISTA或ADMM,将数据忠诚术语和深密密纽斯结合起来。 现有的可证实的PnP方法对取消或忠诚功能,例如非扩大或严格的融和性,施加了严格的限制。 在这项工作中,我们提出了一个可证实的PnP方法,根据近氧化物设定相对轻的条件,并引入准纽顿步骤,以大大加速趋同。我们特别将深度去诺化器参数作为梯度步骤,从而进一步确定准牛顿 PnP 算法的固定点。</s>