Recently, Plug-and-Play (PnP) methods, which integrate splitting algorithms with denoiser priors, have demonstrated state-of-the-art performance in image restoration tasks. The convergence properties of PnP-ADMM can be effectively analyzed from the standpoint of Douglas-Rachford splitting (DRS) methods. Achieving faster fixed-point residuals in splitting-based PnP methods remains a critical challenge. To tackle this theoretical problem, we propose the PnP-dPPM algorithm, which combines the accelerated degenerate proximal point method (dPPM) with PnP priors for enhanced image restoration. Numerical experiments reveal that PnP-dPPM not only accelerates fixed-point convergence but also obtains competitive or even outstanding performance compared to existing PnP methods.