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Automated digital holographic image reconstruction with deep convolutional neural networks

Title
Automated digital holographic image reconstruction with deep convolutional neural networks
Authors
Moon, InkyuJaferzadeh, Keyvan
DGIST Authors
Moon, Inkyu
Issue Date
2020-04-27
Citation
SPIE Defense + Commercial Sensing, 2020(Three-Dimensional Imaging, Visualization, and Display 2020)
Type
Conference
ISBN
9781510635814
ISSN
0277-786X
Abstract
In off-axis digital holographic microscopy, a camera records the spatial interference intensity pattern between light scattered from the specimen and the unperturbed reference light. Digital propagation using the numerical reconstruction algorithm allows both phase-contrast and amplitude-contrast images of the sample to be retrieved. This is possible when the exact distance between the image sensor (such as CCD) plane and image plane is provided. In this paper, we give an overview of our work on a deep-learning convolutional neural network with a regression layer as the top layer to estimate the best focus distance. The experimental results obtained using microsphere beads and red blood cells show that the proposed method can accurately estimate the propagation distance from a filtered hologram. This method can significantly accelerate the numerical reconstruction time since the correct focus is provided by the CNN model with no need for digital propagation at different distances. © 2020 SPIE. Downloading of the abstract is permitted for personal use only.
URI
http://hdl.handle.net/20.500.11750/12881
DOI
10.1117/12.2554533
Publisher
SPIE
Related Researcher
  • Author Moon, Inkyu Intelligent Imaging and Vision Systems Laboratory
  • Research Interests
Files:
There are no files associated with this item.
Collection:
Department of Robotics EngineeringIntelligent Imaging and Vision Systems Laboratory2. Conference Papers


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