\section{Related Work}
In the niche of histology imaging, there have only recently been efforts to restore artifacts, in contrast to the extensive work in general computer vision where object removal and image inpainting is a long-standing topic~\cite{zhang2023image}. In standard computer vision applications, techniques for object removal, such as RePaint~\cite{lugmayr2022repaint}, Palette~\cite{saharia2022palette}, DDRM~\cite{kawar2022denoising}, or GAN-based networks~\cite{zhang2020ris}, have been employed to address issues like shadow elimination and image blurring. These methods, however, typically hinge on certain prerequisites: target predictability, pre-defined object masks, clean image pairs, or extensive datasets~\cite{xiang2023deep}.

Despite these advancements, these methods have limited direct application for histopathological image restoration, and data scarcity and annotation costs are rampant issues in medical imaging. %Medical images demand precision in maintaining the integrity of original images in order not to falsify the diagnosis. 
In recent years, many papers have tackled the topic of stain normalization~\cite{faryna2021tailoring,wagner2022federated} and data augmentation~\cite{TELLEZ2019101544} in order to improve model generalization. However, the domain of artifact restoration in histopathological images has only seen the emergence of the first supervised methodologies\cite{dahan2022artifact,ke2023artifact,he2023artifact}, requiring considerable manual input or supervision on artifacts for effective WSI restoration. While these approaches represent innovative strides, they are often impractical for real-world application due to their inability to cope with unseen artifacts or require costly manual input.  Consequently, their assessments lack real clinical settings complexities and variability. These inherent limitations undermine the reliability and are impractical in clinical practice. %\cite{roschewitz2023automatic} have proposed an approach to detect and correct drifts in data, like artifacts; however, their approach does not allow visual inspection. 
HARP addresses this gap by proposing an approach that focuses on preserving critical features, thereby ensuring that the artifacts are restored without compromising the fidelity of the histological images.