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

ArXi:2603.23647v1 Announce Type: cross 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.