Add to ORKGPhase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing,...
This electronic version was submitted by the student author. The certified thesis is available in th...
In this paper, we employ a deep convolutional neural network for the solution of the phase retrieval...
The reconstruction of a single-particle image from the modulus of its Fourier transform, by phase-re...
Phase retrieval approaches based on deep learning DL provide a framework to obtain phase informat...
Phase retrieval approaches based on deep learning DL provide a framework to obtain phase informat...
Quantitative phase imaging has been of interest to the science and engineering community and has bee...
The non-linear equation of phase retrieval appears in many different scenarios, from X-ray imaging t...
Phase retrieval approaches based on deep learning (DL) provide a framework to obtain phase informati...
Phase retrieval approaches based on deep learning (DL) provide a framework to obtain phase informati...
Phase-retrieval techniques aim to recover the original signal from just the modulus of its Fourier t...
The phase extraction neural network (PhENN) [Optica 4, 1117 (2017)] is a computational architecture,...
The classical phase retrieval problem is the recovery of a constrained image from the magnitude of i...
© 2020 Optical Society of America. Deep learning (DL) has been applied extensively in many computati...
In this paper, we employ a deep convolutional neural network for the solution of the phase retrieval...
© 2020 Optical Society of America. Deep learning (DL) has been applied extensively in many computati...
This electronic version was submitted by the student author. The certified thesis is available in th...
In this paper, we employ a deep convolutional neural network for the solution of the phase retrieval...
The reconstruction of a single-particle image from the modulus of its Fourier transform, by phase-re...
Phase retrieval approaches based on deep learning DL provide a framework to obtain phase informat...
Phase retrieval approaches based on deep learning DL provide a framework to obtain phase informat...
Quantitative phase imaging has been of interest to the science and engineering community and has bee...
The non-linear equation of phase retrieval appears in many different scenarios, from X-ray imaging t...
Phase retrieval approaches based on deep learning (DL) provide a framework to obtain phase informati...
Phase retrieval approaches based on deep learning (DL) provide a framework to obtain phase informati...
Phase-retrieval techniques aim to recover the original signal from just the modulus of its Fourier t...
The phase extraction neural network (PhENN) [Optica 4, 1117 (2017)] is a computational architecture,...
The classical phase retrieval problem is the recovery of a constrained image from the magnitude of i...
© 2020 Optical Society of America. Deep learning (DL) has been applied extensively in many computati...
In this paper, we employ a deep convolutional neural network for the solution of the phase retrieval...
© 2020 Optical Society of America. Deep learning (DL) has been applied extensively in many computati...
This electronic version was submitted by the student author. The certified thesis is available in th...
In this paper, we employ a deep convolutional neural network for the solution of the phase retrieval...
The reconstruction of a single-particle image from the modulus of its Fourier transform, by phase-re...