Abstract
Phase retrieval approaches based on deep learning (DL) provide a framework to obtain phase information from an intensity hologram or diffraction pattern in a robust manner and in real-time. However, current DL architectures applied to the phase problem rely on i) paired datasets, i. e., they are only applicable when a satisfactory solution of the phase problem has been found, and ii) the fact that most of them ignore the physics of the imaging process. Here, we present PhaseGAN, a new DL approach based on Generative Adversarial Networks, which allows the use of unpaired datasets and includes the physics of image formation. The performance of our approach is enhanced by including the image formation physics and a novel Fourier loss function, providing phase reconstructions when conventional phase retrieval algorithms fail, such as ultra-fast experiments. Thus, PhaseGAN offers the opportunity to address the phase problem in real-time when no phase reconstructions but good simulations or data from other experiments are available.
Highlights
Phase retrieval, i. e., reconstructing phase information from intensity measurements, is a common problem in coherent imaging techniques such as holography [1], coherent diffraction imaging [2], and ptychography [3,4]
We applied PhaseGAN to experimental data recorded at the Advanced Photon Source (APS), where unpaired data of metallic foams was recorded with two different detectors at independent sensing experiments
The cyclic structure of PhaseGAN allows to include the physics of image formation in the learning loop, which further enhances the capabilities of unpaired DL approaches, such as CycleGAN
Summary
I. e., reconstructing phase information from intensity measurements, is a common problem in coherent imaging techniques such as holography [1], coherent diffraction imaging [2], and ptychography [3,4]. Examples of deterministic solutions to holography are transport of intensity equations (TIE) [8] or based on Contrast Transfer Functions (CTFs) [9]. Such deterministic approaches can only be applied if certain constraints are met. Iterative approaches are not limited by these constraints [11,12] and can address holography and coherent diffraction imaging and ptychography
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
