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 …

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” …

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 …

Cameras are designed with a complicated tradeoff between image quality (eg sharpness,
contrast, color fidelity), and practical considerations such as cost, form factor, and weight …

Abstract High-dynamic-range (HDR) imaging is crucial for many applications. Yet, acquiring
HDR images with a single shot remains a challenging problem. Whereas modern deep …

Computational microscopy, as a subfield of computational imaging, combines optical
manipulation and image algorithmic reconstruction to recover multi-dimensional microscopic …

Ptychography is an enabling microscopy technique for both fundamental and applied
sciences. In the past decade, it has become an indispensable imaging tool in most X-ray …

Fourier ptychographic microscopy (FPM) is a promising and fast-growing computational
imaging technique with high resolution, wide field-of-view (FOV) and quantitative phase …

Deep neural networks have emerged as effective tools for computational imaging, including
quantitative phase microscopy of transparent samples. To reconstruct phase from intensity …

High-dynamic range (HDR) imaging is an essential imaging modality for a wide range of
applications in uncontrolled environments, including autonomous driving, robotics, and …