Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

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 …

The competition between resolution and the imaging field of view is a long-standing problem
in traditional imaging systems—they can produce either an image of a small area with fine …

Most of the neural networks proposed so far for computational imaging (CI) in optics employ
a supervised training strategy, and thus need a large training set to optimize their weights …

Phase recovery (PR) refers to calculating the phase of the light field from its intensity
measurements. As exemplified from quantitative phase imaging and coherent diffraction …

The goal of computer-generated holography (CGH) is to synthesize custom illumination
patterns by modulating a coherent light beam. CGH algorithms typically rely on iterative …

With the explosive growth of mathematical optimization and computing hardware, deep
neural networks (DNN) have become tremendously powerful tools to solve many …

Simultaneous imaging of various facets of intact biological systems across multiple
spatiotemporal scales is a long-standing goal in biology and medicine, for which progress is …

Traditional imaging systems exhibit a well-known trade-off between the resolution and the
field of view of their captured images. Typical cameras and microscopes can either “zoom in” …

Optical imaging has served as a primary method to collect information about biosystems
across scales—from functionalities of tissues to morphological structures of cells and even at …