Robust Plug-and-Play Framework for Adaptive Image Restoration

Plug-and-Play (PnP) image restoration provides a flexible framework that integrates model-based optimization with powerful deep denoising priors. However, PnP methods based on deep neural networks are highly sensitive to perturbations. In this work, we propose a robust PnP framework embedding a frequency-domain robust correction block and an adaptive noise-level scheduling block for adaptive image restoration. The frequency-domain block selectively suppresses corrupted high-frequency components to align adversarial noise with AWGN assumptions, serving as a general-purpose preprocessing strategy that can be applied beyond PnP. The adaptive noise-level scheduling block based on patch-wise PCA estimation dynamically adjusts the denoiser noise level throughout the iterative process. Experiments on deblurring and superresolution demonstrate that our method achieves stronger robustness and restoration performance than existing PnP methods under adversarial attacks. Ablation studies validate the complementary contributions of the two proposed blocks.