Existing image reconstruction algorithms for ghost imaging often struggle to balance reconstruction performance and broad applicability. To address this issue, we propose a snapshot ghost imaging reconstruction algorithm based on the Plug-and-Play Alternating Direction Method of Multipliers (PnP-ADMM) framework. By seamlessly integrating high-performance denoisers, the algorithm significantly enhances reconstruction quality across both low and high sampling rates. Specifically, when employing the deep convolutional neural network FFDNet as the denoiser, the method effectively combines the strengths of model-driven and learning-driven approaches, surpassing the limitations of traditional compressed sensing and deep learning methods. Experimental results show that, compared to TVNLR, which utilizes total variation and low-rank constraints, PnP-ADMM (FFDNet) achieves a PSNR improvement exceeding 2 dB and an SSIM improvement greater than 0.15, while effectively suppressing reconstruction noise and preserving intricate image details.