Unsupervised feature learning and clustering of particles imaged in raw holograms using an autoencoder

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.