Abstract In 1948, Dennis Gabor proposed the concept of holography, providing a pioneering
solution to a quantitative description of the optical wavefront. After 75 years of development …

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 …

Phase recovery from intensity-only measurements forms the heart of coherent imaging
techniques and holography. In this study, we demonstrate that a neural network can learn to …

Deep learning-based image reconstruction methods have achieved remarkable success in
phase recovery and holographic imaging. However, the generalization of their image …

Holography encodes the three-dimensional (3D) information of a sample in the form of an
intensity-only recording. However, to decode the original sample image from its hologram …

Holography provides access to the optical phase. The emerging compressive phase
retrieval approach can achieve in-line holographic imaging beyond the information-theoretic …

Accurate depiction of waves in temporal and spatial is essential to the investigation of
interactions between physical objects and waves. Digital holography (DH) can perform …

Early identification of pathogenic bacteria in food, water, and bodily fluids is very important
and yet challenging, owing to sample complexities and large sample volumes that need to …

Optical compound microscope has been a major tool in biomedical imaging for centuries. Its
performance relies on relatively complicated, bulky and expensive lenses and alignment …

We present a deep learning framework based on a generative adversarial network (GAN) to
perform super-resolution in coherent imaging systems. We demonstrate that this framework …