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The development of signal unmixing algorithms is essential for leveragingmultimodal datasets acquired through a wide array of scientific imagingtechnologies, including hyperspectral or time-resolved acquisitions. Inexperimental physics, enhancing the spatio-temporal resolution or expanding thenumber of detection channels often leads to diminished sampling rate andsignal-to-noise ratio (SNR), significantly affecting the efficacy of signalunmixing algorithms. We propose Latent Unmixing, a new approach which appliesband-pass filters to the latent space of a multi-dimensional convolutionalneural network to disentangle overlapping signal components. It enables betterisolation and quantification of individual signal contributions, especially inthe context of undersampled distributions. Using multi-dimensional convolutionkernels to process all dimensions simultaneously enhances the network's abilityto extract information from adjacent pixels, and time- or spectral-bins. Thisapproach enables more effective separation of components in cases whereindividual pixels do not provide clear, well-resolved information. We showcasethe method's practical use in experimental physics through two test cases thathighlight the versatility of our approach: fluorescence lifetime microscopy andmode decomposition in optical fibers. The latent unmixing method extractsvaluable information from complex signals that cannot be resolved by standardmethods. It opens new possibilities in optics and photonics for multichannelseparations at an increased sampling rate.

Unmixing Optical Signals from Undersampled Volumetric Measurements by Filtering the Pixel Latent Variables