Here we elaborate on the edge-enhanced spectral components that are produced by the
vortex Fourier transform, which are introduced in [1]. The vortex phase pattern imprinted
on from an object breaks the spatial invariance of its Fourier representation is robust
to noise. We report on new results related to the image classification of the MNIST
digit dataset with no hidden layers. We show that the accuracy from one phase vortex
mask is capable of achieving 0.95 validation accuracy and further show that the dynamic
range of the phase modulation scheme significantly influences the classification accuracy
and classification convergence rate.
