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Found 6 papers, 3 papers with code
Coordinate-based neural representations for computational adaptive optics in widefield microscopy
We implemented CoCoA for widefield imaging of mouse brain tissues and validated its performance with direct-wavefront-sensing-based adaptive optics.
Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits
Two scanning operations are required: ptychographic to recover the complex transmissivity of the specimen; and rotation of the specimen to acquire multiple projections covering the 3D spatial frequency domain.
Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time
Noninvasive X-ray imaging of nanoscale three-dimensional objects, e. g. integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational and returns estimates of complex electromagnetic field through ICs; and tomographic scanning, which collects complex field projections from multiple angles.
Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning
Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem with practical implications in medical and biological imaging, manufacturing, automation, and environmental and food security.
On the interplay between physical and content priors in deep learning for computational imaging
That is, the higher the entropy of the training images, the weaker the regularization effect can be imposed.
Learning to Synthesize: Robust Phase Retrieval at Low Photon counts
The quality of inverse problem solutions obtained through deep learning [Barbastathis et al, 2019] is limited by the nature of the priors learned from examples presented during the training phase.
