Imaging Mass Cytometry (IMC) is an emerging multiplexed imaging technology for analyzing complex microenvironments thathas the ability todetect the spatial distribution of at least 40 cell markers. However, this new modality has unique image data processing requirements, particularly when applying this technology to patient tissue specimens. In these cases, signal-to-noise ratio (SNR) forparticular markerscan be low despite optimization of staining conditions, and the presence of pixel intensity artifacts can deteriorate image quality and the performance of downstream analysis. Here we demonstrate a content-aware pipeline, IMC-Denoise, to restore IMC images. Specifically, we deploy a differential intensity map-based restoration (DIMR) algorithm for removing hot pixels and a self-supervised deep learning algorithm for filtering photon shot noise (DeepSNF). IMC-Denoise outperforms existing methods for adaptive hot pixel removal without loss of resolution and delivers significant SNR improvement to a diverse set of IMC channels and datasets, including a technically challenging unique human bone marrow IMC dataset. Moreover, with cell-scale analysis on this bone marrow data, our approach reduces noise variability in modeling of intercell communications, enhances cell phenotyping including T cell subset-specific biological interpretations.