{\lambda}Split: Self-Supervised Content-Aware Spectral Unmixing for Fluorescence Microscopy

arXiv:2603.23647v2 Announce Type: replace-cross Abstract: In fluorescence microscopy, spectral unmixing aims to recover individual fluorophore concentrations from spectral images that capture mixed fluorophore emissions. Since classical methods operate pixel-wise and rely on least-squares fitting, their performance degrades with increasingly overlapping emission spectra and higher levels of noise, suggesting that a data-driven approach that can learn and utilize a structural prior might lead to improved results. Learning-based approaches for spectral imaging do exist, but they are either not o

Source: arXiv cs.LG — read the full report at the original publisher.

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