Denoising noisy datasets is a crucial task in this data-driven world. In this paper, we develop a persistence-guided discrete Morse theoretic denoising framework. We use our method to denoise point clouds and to extract surfaces from noisy volumes. In addition, we show that our method generally outperforms standard methods. Our paper is a synergy of classical noise removal techniques and topological data analysis.