scholarly journals Quantitative Phase Imaging in Digital Holographic Microscopy Based on Image Inpainting Using a Two-Stage Generative Adversarial Network

2021 ◽  
Author(s):  
Shujun Ma ◽  
Qi Liu ◽  
Yantao Yu ◽  
Yu Luo ◽  
Shiliang Wang
Keyword(s):  
Image Inpainting ◽  
Digital Holographic Microscopy ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Two Stage ◽  
Generative Adversarial Network ◽  
Quantitative Phase ◽  
Adversarial Network ◽  
Holographic Microscopy

scholarly journals Video-Rate Quantitative Phase Imaging Using a Digital Holographic Microscope and a Generative Adversarial Network

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8021
Author(s):  
Raul Castaneda ◽  
Carlos Trujillo ◽  
Ana Doblas
Keyword(s):  
Digital Holographic Microscopy ◽  
Reconstruction Method ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Generative Adversarial Network ◽  
Experimental Conditions ◽  
Quantitative Phase ◽  
Human Red Blood Cells ◽  
Adversarial Network ◽  
Phase Images

The conventional reconstruction method of off-axis digital holographic microscopy (DHM) relies on computational processing that involves spatial filtering of the sample spectrum and tilt compensation between the interfering waves to accurately reconstruct the phase of a biological sample. Additional computational procedures such as numerical focusing may be needed to reconstruct free-of-distortion quantitative phase images based on the optical configuration of the DHM system. Regardless of the implementation, any DHM computational processing leads to long processing times, hampering the use of DHM for video-rate renderings of dynamic biological processes. In this study, we report on a conditional generative adversarial network (cGAN) for robust and fast quantitative phase imaging in DHM. The reconstructed phase images provided by the GAN model present stable background levels, enhancing the visualization of the specimens for different experimental conditions in which the conventional approach often fails. The proposed learning-based method was trained and validated using human red blood cells recorded on an off-axis Mach–Zehnder DHM system. After proper training, the proposed GAN yields a computationally efficient method, reconstructing DHM images seven times faster than conventional computational approaches.

Experimental Study on Quantitative Phase Imaging by Digital Holographic Microscopy

2012 ◽  
Vol 39 (3) ◽  
pp. 0309002 ◽  
Author(s):  
马利红 Ma Lihong ◽  
王辉 Wang Hui ◽  
金洪震 Jin Hongzhen ◽  
李勇 Li Yong
Keyword(s):  
Experimental Study ◽  
Digital Holographic Microscopy ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase ◽  
Holographic Microscopy

Shift-variant digital holographic microscopy: inaccuracies in quantitative phase imaging

2013 ◽  
Vol 38 (8) ◽  
pp. 1352 ◽  
Author(s):  
Ana Doblas ◽  
Emilio Sánchez-Ortiga ◽  
Manuel Martínez-Corral ◽  
Genaro Saavedra ◽  
Pedro Andrés ◽  
...  
Keyword(s):  
Digital Holographic Microscopy ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase ◽  
Holographic Microscopy
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