• Corpus ID: 209515363

Machine learning holography for measuring 3D particle size distribution

  title={Machine learning holography for measuring 3D particle size distribution},
  author={Siyao Shao and Kevin Mallery and Jiarong Hong},
  journal={arXiv: Applied Physics},
Particle size measurement based on digital holography with conventional algorithms are usually time-consuming and susceptible to noises associated with hologram quality and particle complexity, limiting its usage in a broad range of engineering applications and fundamental research. We propose a learning-based hologram processing method to cope with the aforementioned issues. The proposed approach uses a modified U-net architecture with three input channels and two output channels, and… 

Figures from this paper

Machine learning shadowgraph for particle size and shape characterization

A robust learning-based method using a single convolution neural network for analyzing particle shadow images using a two-channel-output U-net model and a particle centroid image is introduced.

Holographic astigmatic particle tracking velocimetry (HAPTV)

The formation of twin images in digital inline holography (DIH) prevents the placement of the focal plane in the center of a sample volume for DIH-based particle tracking velocimetry (DIH-PTV) with a



Machine learning holography for 3D particle field imaging.

We propose a new learning-based approach for 3D particle field imaging using holography. Our approach uses a U-net architecture incorporating residual connections, Swish activation, hologram

Digital holographic particle volume reconstruction using a deep neural network

A particle volume reconstruction directly from an in-line hologram using a deep neural network (DNN) is proposed, which can simultaneously detect the lateral and axial positions, and the particle sizes via a DNN.

Machine-learning approach to holographic particle characterization.

Here, it is demonstrated that machine-learning techniques based on support vector machines (SVMs) can analyze holographic video microscopy data in real time on low-power computers.

Fast particle characterization using digital holography and neural networks.

We propose using a neural network approach in conjunction with digital holographic microscopy in order to rapidly determine relevant parameters such as the core and shell diameter of coated,

Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles.

It is demonstrated that two popular machine-learning algorithms, cascade classifiers and deep convolutional neural networks (CNN) can solve the feature-localization problem orders of magnitude faster than current state-of-the-art techniques.

Regularized inverse holographic volume reconstruction for 3D particle tracking.

This work utilizes an inverse problem method with fused lasso regularization to perform full volumetric reconstructions of particle fields by exploiting data sparsity in the solution and utilizing GPU processing to dramatically reduce the computational cost usually associated with inverse reconstruction approaches.

Refinement of particle detection by the hybrid method in digital in-line holography.

A refinement procedure is developed that distinguishes such erroneous particles from accurately detected ones and further separates individual particles in digital in-line holography and confirms the usefulness of the proposed method.

Learning-based nonparametric autofocusing for digital holography

distance estimation is converted to hologram prediction, which is solved by designing a powerful convolutional neural network trained by a set of holograms acquired a priori, which allows fast autofocusing without reconstructing an image stack, even when the physical parameters of the optical setup are unknown.

A hybrid image processing method for measuring 3D bubble distribution using digital inline holography