Abstract
Digital holographic imaging can capture a volume of a particle field and reconstruct three-dimensional (3D) information of the volume from a two-dimensional (2D) hologram. However, it experiences a DC term, twin-images, defocus images of other particles and noise induced by the optical system. We propose the use of a U-net model to extract in-focus particles and encode the in-focus particles as squares at ground truth z. Meanwhile, zero-order images, twin-images, defocused images of other particle and noise induced by the optical system are filtered out. The central coordinate of the square represents the lateral position of the particle, and the side length of the square represents the particle diameter. The 2D raw-reconstructed images generated from the pre-processed hologram by utilizing backward Fresnel propagation serve as the input of the network. A dense block is designed and added to the encoder and decoder of the traditional U-net model. Each layer takes the inputs from all previous layers and passes the feature maps to all subsequent layers, thereby facilitating full characterization of the particles. The results show that the proposed U-net model can extract overlapping particles along the z-axis well, allowing the detection of dense particles. The use of that squares characterize particles makes it more convenient to obtain particle parameters.
Highlights
The particle fields comprise small objects, such as bubbles, biological cells, droplets
255 raw-reconstructed images corresponding to 51 holograms and 255 ground truth images are used as the dataset to train the U-net models
Because the particle field contains multiple transparent particles, we observe that the raw-reconstructed volume shown in Figure 2B is full of noise, including the zeros-order images, conjugate images, and defocused images of the other particles
Summary
The particle fields comprise small objects, such as bubbles, biological cells, droplets. Owning to only a single hologram can be reconstructed to restore the 3D information of the objects, DH has emerged as a powerful tool for 3D imaging in recent years. The minimum intensity was applied to detect the edges of the bubbles from raw-reconstructed images. The minimum intensity method depends on the threshold setting to distinguish the particles from the background. Various criteria (such as edge sharpness and intensity distribution) [15, 17, 18] were applied to characterize the focus level of particles. These criteria are sensitive to the detailed
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