  Single-pixel imaging (SPI) is a novel, unconventional method that goes beyond
the notion of traditional cameras but can be computationally expensive and slow
for real-time applications. Deep learning has been proposed as an alternative
approach for solving the SPI reconstruction problem, but a detailed analysis of
its performance and generated basis patterns when used for SPI is limited. We
present a modified deep convolutional autoencoder network (DCAN) for SPI on
64x64 pixel images with up to 6.25% compression ratio and apply binary and
orthogonality regularizers during training. Training a DCAN with these
regularizers allows it to learn multiple measurement bases that have
combinations of binary or non-binary, and orthogonal or non-orthogonal
patterns. We compare the reconstruction quality, orthogonality of the patterns,
and robustness to noise of the resulting DCAN models to traditional SPI
reconstruction algorithms (such as Total Variation minimization and Fourier
Transform). Our DCAN models can be trained to be robust to noise while still
having fast enough reconstruction times (~3 ms per frame) to be viable for
real-time imaging.
