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Spectral pre-modulation of training examples enhances the spatial resolution of the Phase Extraction Neural Network (PhENN)
The Phase Extraction Neural Network (PhENN) is a computational architecture,
based on deep machine learning, for lens-less quantitative phase retrieval from
raw intensity data. PhENN is a deep convolutional neural network trained
through examples consisting of pairs of true phase objects and their
corresponding intensity diffraction patterns; thereafter, given a test raw
intensity pattern PhENN is capable of reconstructing the original phase object
robustly, in many cases even for objects outside the database where the
training examples were drawn from. Here, we show that the spatial frequency
content of the training examples is an important factor limiting PhENN's
spatial frequency response. For example, if the training database is relatively
sparse in high spatial frequencies, as most natural scenes are, PhENN's ability
to resolve fine spatial features in test patterns will be correspondingly
limited. To combat this issue, we propose "flattening" the power spectral
density of the training examples before presenting them to PhENN. For phase
objects following the statistics of natural scenes, we demonstrate
experimentally that the spectral pre-modulation method enhances the spatial
resolution of PhENN by a factor of 2.Comment: 12 pages, 10 figure
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