AIMC Topic: Holography

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Classification of Holograms with 3D-CNN.

Sensors (Basel, Switzerland)
A hologram, measured by using appropriate coherent illumination, records all substantial volumetric information of the measured sample. It is encoded in its interference patterns and, from these, the image of the sample objects can be reconstructed i...

Real-time complex light field generation through a multi-core fiber with deep learning.

Scientific reports
The generation of tailored complex light fields with multi-core fiber (MCF) lensless microendoscopes is widely used in biomedicine. However, the computer-generated holograms (CGHs) used for such applications are typically generated by iterative algor...

Deep Learning-Based Phenotypic Assessment of Red Cell Storage Lesions for Safe Transfusions.

IEEE journal of biomedical and health informatics
This study presents a novel approach to automatically perform instant phenotypic assessment of red blood cell (RBC) storage lesion in phase images obtained by digital holographic microscopy. The proposed model combines a generative adversarial networ...

Pattern Recognition of Holographic Image Library Based on Deep Learning.

Journal of healthcare engineering
The final loss function in the deep learning neural network is composed of other functions in the network. Due to the existence of a large number of non-linear functions such as activation functions in the network, the entire deep learning model pres...

Speeding up reconstruction of 3D tomograms in holographic flow cytometry deep learning.

Lab on a chip
Tomographic flow cytometry by digital holography is an emerging imaging modality capable of collecting multiple views of moving and rotating cells with the aim of recovering their refractive index distribution in 3D. Although this modality allows us ...

Single Cell Analysis of Stored Red Blood Cells Using Ultra-High Throughput Holographic Cytometry.

Cells
Holographic cytometry is introduced as an ultra-high throughput implementation of quantitative phase imaging of single cells flowing through parallel microfluidic channels. Here, the approach was applied for characterizing the morphology of individua...

Quantitative particle agglutination assay for point-of-care testing using mobile holographic imaging and deep learning.

Lab on a chip
Particle agglutination assays are widely adopted immunological tests that are based on antigen-antibody interactions. Antibody-coated microscopic particles are mixed with a test sample that potentially contains the target antigen, as a result of whic...

Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Quantitative phase imaging (QPI) is an emerging label-free technique that produces images containing morphological and dynamical information without contrast agents. Unfortunately, the phase is wrapped in most imaging system. Phase unwrapping is the ...

Towards real-time photorealistic 3D holography with deep neural networks.

Nature
The ability to present three-dimensional (3D) scenes with continuous depth sensation has a profound impact on virtual and augmented reality, human-computer interaction, education and training. Computer-generated holography (CGH) enables high-spatio-a...

Learning Diatoms Classification from a Dry Test Slide by Holographic Microscopy.

Sensors (Basel, Switzerland)
Diatoms are among the dominant phytoplankters in marine and freshwater habitats, and important biomarkers of water quality, making their identification and classification one of the current challenges for environmental monitoring. To date, taxonomy o...