On the use of deep learning for phase recovery
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 …
measurements. As exemplified from quantitative phase imaging and coherent diffraction …
Deep learning spatial phase unwrapping: a comparative review
Phase unwrapping is an indispensable step for many optical imaging and metrology
techniques. The rapid development of deep learning has brought ideas to phase …
techniques. The rapid development of deep learning has brought ideas to phase …
Far-field super-resolution ghost imaging with a deep neural network constraint
F Wang, C Wang, M Chen, W Gong, Y Zhang… - Light: Science & …, 2022 - nature.com
Ghost imaging (GI) facilitates image acquisition under low-light conditions by single-pixel
measurements and thus has great potential in applications in various fields ranging from …
measurements and thus has great potential in applications in various fields ranging from …
Deep-learning electron diffractive imaging
We report the development of deep-learning coherent electron diffractive imaging at
subangstrom resolution using convolutional neural networks (CNNs) trained with only …
subangstrom resolution using convolutional neural networks (CNNs) trained with only …
Different channels to transmit information in scattering media
X Zhang, J Gao, Y Gan, C Song, D Zhang, S Zhuang… - PhotoniX, 2023 - Springer
A communication channel should be built to transmit information from one place to another.
Imaging is 2 or higher dimensional information communication. Conventionally, an imaging …
Imaging is 2 or higher dimensional information communication. Conventionally, an imaging …
Phase extraction neural network (PhENN) with coherent modulation imaging (CMI) for phase retrieval at low photon counts
Imaging with low-dose light is of importance in various fields, especially when minimizing
radiation-induced damage onto samples is desirable. The raw image captured at the …
radiation-induced damage onto samples is desirable. The raw image captured at the …
Multiple-scattering simulator-trained neural network for intensity diffraction tomography
Recovering 3D phase features of complex biological samples traditionally sacrifices
computational efficiency and processing time for physical model accuracy and …
computational efficiency and processing time for physical model accuracy and …
Physics-informed computer vision: A review and perspectives
The incorporation of physical information in machine learning frameworks is opening and
transforming many application domains. Here the learning process is augmented through …
transforming many application domains. Here the learning process is augmented through …
Displacement-agnostic coherent imaging through scatter with an interpretable deep neural network
Coherent imaging through scatter is a challenging task. Both model-based and data-driven
approaches have been explored to solve the inverse scattering problem. In our previous …
approaches have been explored to solve the inverse scattering problem. In our previous …
Two-step training deep learning framework for computational imaging without physics priors
Deep learning (DL) is a powerful tool in computational imaging for many applications. A
common strategy is to use a preprocessor to reconstruct a preliminary image as the input to …
common strategy is to use a preprocessor to reconstruct a preliminary image as the input to …