Cited 4 time in webofscience Cited 5 time in scopus

Noise-free quantitative phase imaging in Gabor holography with conditional generative adversarial network

Title
Noise-free quantitative phase imaging in Gabor holography with conditional generative adversarial network
Authors
Moon, InkyuJaferzadeh, KeyvanKim, YouhyunJavidi, Bahram
DGIST Authors
Moon, Inkyu; Kim, Youhyun
Issue Date
2020-08
Citation
Optics Express, 28(18), 26284-26301
Type
Article
Article Type
Article
Keywords
SHIFTING DIGITAL HOLOGRAPHYWAVE-FRONT RECONSTRUCTIONIN-LINE HOLOGRAPHYMICROSCOPYIDENTIFICATIONELIMINATIONTRACKING
ISSN
1094-4087
Abstract
This paper shows that deep learning can eliminate the superimposed twin-image noise in phase images of Gabor holographic setup. This is achieved by the conditional generative adversarial model (C-GAN), trained by input-output pairs of noisy phase images obtained from synthetic Gabor holography and the corresponding quantitative noise-free contrast-phase image obtained by the off-axis digital holography. To train the model, Gabor holograms are generated from digital off-axis holograms with spatial shifting of the real image and twin image in the frequency domain and then adding them with the DC term in the spatial domain. Finally, the digital propagation of the Gabor hologram with Fresnel approximation generates a super-imposed phase image for the C-GAN model input. Two models were trained: a human red blood cell model and an elliptical cancer cell model. Following the training, several quantitative analyses were conducted on the bio-chemical properties and similarity between actual noise-free phase images and the model output. Surprisingly, it is discovered that our model can recover other elliptical cell lines that were not observed during the training. Additionally, some misalignments can also be compensated with the trained model. Particularly, if the reconstruction distance is somewhat incorrect, this model can still retrieve in-focus images. © 2020 Optical Society of America
URI
http://hdl.handle.net/20.500.11750/12631
DOI
10.1364/OE.398528
Publisher
Optical Society of America
Related Researcher
  • Author Moon, Inkyu Intelligent Imaging and Vision Systems Laboratory
  • Research Interests
Files:
Collection:
Department of Robotics EngineeringIntelligent Imaging and Vision Systems Laboratory1. Journal Articles
Department of Robotics EngineeringRobotics Engineering Research Center1. Journal Articles


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