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 …

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

Recent years have witnessed the unprecedented progress of deep learning applications in
digital holography (DH). Nevertheless, there remain huge potentials in how deep learning …

Deep learning has been developing rapidly, and many holographic applications have been
investigated using deep learning. They have shown that deep learning can outperform …

Existing applications of deep learning in computational imaging and microscopy mostly
depend on supervised learning, requiring large-scale, diverse and labelled training data …

Holographic imaging poses the ill posed inverse mapping problem of retrieving complex
amplitude maps from measured diffraction intensity patterns. The existing deep learning …

Digital holography is a 3D imaging technique by emitting a laser beam with a plane
wavefront to an object and measuring the intensity of the diffracted waveform, called …

The problem of phase retrieval underlies various imaging methods from astronomy to
nanoscale imaging. Traditional phase retrieval methods are iterative and are therefore …

With the success of deep learning methods in many image processing tasks, deep learning
approaches have also been introduced to the phase retrieval problem recently. These …

Deep-learning methods using unpaired datasets hold great potential for image
reconstruction, especially in biomedical imaging where obtaining paired datasets is often …