Figure 1.

Combining off-axis holography and deep learning to characterize subwavelength particles. (a) Interference pattern is acquired with an off-axis holographic microscope and (b) Fourier-transformed and low-pass-filtered to produce the reconstructed field at the camera plane (here, only the imaginary part of the complex field is shown). (c) These complex field images are used to track the three-dimensional particle position. (d) Region of interest is selected around each detected particle for each time step. (e) Finally, this sequence of observations is used to characterize the radius r and refractive index n using a specialized neural-network design, which we call weighted average convolutional neural network (WAC-NET). (f) Laser beam is split into two paths by a polarizing beam splitter (PBS). The relative intensities of the two paths are regulated by the initial λ/2-waveplate. One of the beams goes through the sample, and the light scattered from the sample is collected by a microscope objective (MO), after which a tube lens (TL) provides a focused image at the camera (C). The paths are recombined using a beam splitter (BS) at a slight offset angle, resulting in an interference pattern at the camera. The particles are suspended in liquid inside a straight microfluidic chip (ChipShop). Reprinted with permission from ref (25) under the Creative Commons license. (g) Schematic overview of WAC-NET. Each observation is downsampled and flattened using a convolutional network. From this flattened vector, a weight and a representation are calculated and multiplied. The weightings are normalized using a softmax transformation. The weighted representations are merged by summation, and the result is passed to a fully connected network that returns r and n.