  Histological artifacts pose challenges for both pathologists and
Computer-Aided Diagnosis (CAD) systems, leading to errors in analysis. Current
approaches for histological artifact restoration, based on Generative
Adversarial Networks (GANs) and pixel-level Diffusion Models, suffer from
performance limitations and computational inefficiencies. In this paper, we
propose a novel framework, LatentArtiFusion, which leverages the latent
diffusion model (LDM) to reconstruct histological artifacts with high
performance and computational efficiency. Unlike traditional pixel-level
diffusion frameworks, LatentArtiFusion executes the restoration process in a
lower-dimensional latent space, significantly improving computational
efficiency. Moreover, we introduce a novel regional artifact reconstruction
algorithm in latent space to prevent mistransfer in non-artifact regions,
distinguishing our approach from GAN-based methods. Through extensive
experiments on real-world histology datasets, LatentArtiFusion demonstrates
remarkable speed, outperforming state-of-the-art pixel-level diffusion
frameworks by more than 30X. It also consistently surpasses GAN-based methods
by at least 5% across multiple evaluation metrics. Furthermore, we evaluate the
effectiveness of our proposed framework in downstream tissue classification
tasks, showcasing its practical utility. Code is available at
https://github.com/bugs-creator/LatentArtiFusion.
