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Filtering Pixel Latent Variables for Unmixing Noisy and Undersampled Volumetric Images
The development of robust signal unmixing algorithms is essential for leveraging multimodal datasets acquired through a wide array of scientific imaging technologies, including hyperspectral or time-resolved acquisitions.
Learning to Become an Expert: Deep Networks Applied To Super-Resolution Microscopy
More specifically, we are proposing a system based on a deep neural network that can provide a quantitative quality measure of a STED image of neuronal structures given as input.
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