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.
