One-Shot Phase Retrieval Method for Interferometry Using a Multi-Stage Phase-Shifting Network

Zhao, Yan; Hu, Ke; Liu, Fengwei

doi:10.1109/lpt.2022.3222814

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…: 1500 pairs l 1 -norm Hologram generation Zhang et al 88 , 89 Hologram Phase-shifting holograms Y-Net Sim. : --- l 2 -norm Yan et al 91 Hologram Single phase-shifting hologram ResNet Sim: 12,000 pairs GAN loss Zhao et al 92 Hologram Phase-shifting holograms MPRNet Sim. : --- Charbonnier and Edge Huang et al 94 Hologram Phase-shifting holograms Y-Net Expt.…”

Section: Dl-pre-processing For Phase Recoverymentioning

confidence: 99%

“…Since the phase-shift between the generated holograms is equal, Yan et al 91 proposed to generate noise-free phase-shifting holograms using a simple end-to-end generative adversarial network (GAN) in a manner of sequential concatenation. Subsequently, for better performance in balancing spatial details and high-level semantic information, Zhao et al 92 applied the multi-stage progressive image restoration network (MPRNet) 93 for phase-shifting hologram generation. Huang et al 94 and Wu et al 95 then expanded this approach from four-step to three-step and two-step phase-shifting methods, respectively.…”

Section: Dl-pre-processing For Phase Recoverymentioning

confidence: 99%

See 1 more Smart Citation

On the use of deep learning for phase recovery

Wang,

Song,

Wang

et al. 2024

Light Sci Appl

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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 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, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource (https://github.com/kqwang/phase-recovery) for readers to learn more about PR.

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