Artificial intelligence tasks across numerous applications require accelerators for fast and
low-power execution. Optical computing systems may be able to meet these domain-specific …

Recent years have seen the rapid growth and development of the field of smart photonics,
where machine-learning algorithms are being matched to optical systems to add new …

The ability to present three-dimensional (3D) scenes with continuous depth sensation has a
profound impact on virtual and augmented reality, human–computer interaction, education …

Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …

We present deep-learning-enabled super-resolution across different fluorescence
microscopy modalities. This data-driven approach does not require numerical modeling of …

Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …

Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and
label-free investigation of the physiology and pathology of biological systems. This review …

Recent advances in deep learning have given rise to a new paradigm of holographic image
reconstruction and phase recovery techniques with real-time performance. Through data …

Using a deep neural network, we demonstrate a digital staining technique, which we term
PhaseStain, to transform the quantitative phase images (QPI) of label-free tissue sections …

The availability of large datasets has boosted the application of machine learning in many
fields and is now starting to shape active-matter research as well. Machine learning …