The utilisation of Plug-and-Play (PnP) priors in inverse problems has become
increasingly prominent in recent years. This preference is based on the
mathematical equivalence between the general proximal operator and the
regularised denoiser, facilitating the adaptation of various off-the-shelf
denoiser priors to a wide range of inverse problems. However, existing PnP
models predominantly rely on pre-trained denoisers using large datasets. In
this work, we introduce Single-Shot PnP methods (SS-PnP), shifting the focus to
solving inverse problems with minimal data. First, we integrate Single-Shot
proximal denoisers into iterative methods, enabling training with single
instances. Second, we propose implicit neural priors based on a novel function
that preserves relevant frequencies to capture fine details while avoiding the
issue of vanishing gradients. We demonstrate, through extensive numerical and
visual experiments, that our method leads to better approximations.

利用 Plug-and-Play（PnP）先验在逆问题中的应用日益突出，本研究介绍了 Single-Shot PnP 方法（SS-PnP），使用少量数据解决逆问题，通过将 Single-Shot 近端去噪器集成到迭代方法中，并提出基于保留相关频率的新型函数的隐式神经先验，以获得更好的逼近。