Characterization Method for Particle Extraction From Raw-Reconstructed Images Using U-Net

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.