\section{Conclusion}
In conclusion, our work presents the \textbf{Histopathological Artifact Restoration Pipeline (HARP)}, a novel and fully unsupervised approach -- \textbf{the first of its kind}-- for restoring artifacts in histopathological images. HARP efficiently integrates artifact detection, localization, and restoration into one seamless clinical workflow for computational pathology. Our work significantly enhances image quality and diagnostic accuracy by leveraging advanced \textbf{artifact detection}, deploying a \textbf{novel unsupervised artifact localization} technique, and presenting a new \textbf{state-of-the-art inpainting denoising diffusion model}. We evaluate the artifact detection quality along with the image reconstruction quality, surpassing the state-of-the-art artifact restoration methods. Furthermore, we demonstrate that HARP can improve the robustness and reliability of downstream models. Finally, we show that \textbf{pathologists can not tell the difference} between clean images and images processed through HARP. This breakthrough in medical image processing holds immense potential for improving AI systems' reliability in histopathological examinations.