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Express"],"published-print":{"date-parts":[[2025,9,22]]},"abstract":"<jats:p>Multicolor single-molecule localization microscopy (SMLM) enables analysis of nanoscale spatial relationships of multiple targets but suffers from technical complexity. CC-STORM simplifies optics by integrating a colorimetry camera with MLE-Locator and NCI-Classifier, yet its low signal-to-noise ratio and undersampled point spread function necessitate &gt;70% data rejection to maintain &lt;2% crosstalk, severely limiting efficiency. To overcome these limitations, we developed CC-DeepSTORM, a deep learning framework combining CC-DeepLoc for localization and CC-DeepSeparator for color-separation. In simulations, CC-DeepLoc enhanced the Jaccard index and localization precision by 1.8\u00d7 and 2\u00d7 over MLE-Locator. For single-color experimental data, CC-DeepSeparator reduced data rejection from \u223c70% to \u223c40% while achieving 1% crosstalk (matching state-of-the-art). 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