Unsupervised feature learning and clustering of particles imaged in rawholograms using an autoencoder
2021
Digital holography is a useful tool to image microscopic particles.
Reconstructed holograms give high-resolution shape information that can be
used to identify the types of particles. However, the process of
reconstructing holograms is computationally intensive and cannot easily
keep up with the rate of data acquisition on low-power sensor platforms.
In this work, we explore the possibility of performing object clustering
on holograms that have not been reconstructed, i.e., images of raw
interference patterns, using the latent representations of a deep-learning
autoencoder and a self-organizing mapping network in a fully unsupervised
manner. We demonstrate this concept on synthetically generated holograms
of different shapes, where clustering of raw holograms achieves an
accuracy of 94.4%. This is comparable to the 97.4% accuracy achieved using
the reconstructed holograms of the same targets. Directly clustering raw
holograms takes less than 0.1 s per image using a low-power CPU board.
This represents a three-order of magnitude reduction in processing time
compared to clustering of reconstructed holograms and makes it possible to
interpret targets in real time on low-power sensor platforms. Experiments
on real holograms demonstrate significant gains in clustering accuracy
through the use of synthetic holograms to train models. Clustering
accuracy increased from 47.1% when the models were trained only on the
real raw holograms, to 64.1% when the models were entirely trained on the
synthetic raw holograms, and further increased to 75.9% when models were
trained on the both synthetic and real datasets using transfer learning.
These results are broadly comparable to those achieved when reconstructed
holograms are used, where the highest accuracy of 70% achieved when
clustering raw holograms outperforms the highest accuracy achieved when
clustering reconstructed holograms by a significant margin for our
datasets.
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