Computational imaging techniques are growing more popular, but the large number of measurements they require often lead to slow speeds or damage to biological samples. A newly developed physics-informed variational autoencoder (P-VAE) framework could help speed up computational imaging by using supervised learning to jointly reconstruct many light sources, each with sparse measurements.
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Source: Phys.org