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 …

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 …

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 …

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 is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

While recent research has shown that holographic displays can represent photorealistic 3D
holograms in real time, the difficulty in acquiring high-quality real-world holograms has …

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

Holotomography (HT) represents a 3D, label-free optical imaging methodology that
leverages refractive index as an inherent quantitative contrast for imaging. This technique …

The development of deep learning and open access to a substantial collection of imaging
data together provide a potential solution for computational image transformation, which is …

In recent years, rapid developments in imaging concepts and computational methods have
given rise to a new generation of imaging technologies based on chaos. These chaos …