  Covering from photography to depth and spectral estimation, diverse
computational imaging (CI) applications benefit from the versatile modulation
of coded apertures (CAs). The light wave fields as space, time, or spectral can
be modulated to obtain projected encoded information at the sensor that is then
decoded by efficient methods, such as the modern deep learning decoders.
Despite the CA can be fabricated to produce an analog modulation, a binary CA
is mostly preferred since easier calibration, higher speed, and lower storage
are achieved. As the performance of the decoder mainly depends on the structure
of the CA, several works optimize the CA ensembles by customizing regularizers
for a particular application without considering critical physical constraints
of the CAs. This work presents an end-to-end (E2E) deep learning-based
optimization of CAs for CI tasks. The CA design method aims to cover a wide
range of CI problems easily changing the loss function of the deep approach.
The designed loss function includes regularizers to fulfill the widely used
sensing requirements of the CI applications. Mainly, the regularizers can be
selected to optimize the transmittance, the compression ratio, and the
correlation between measurements, while a binary CA solution is encouraged, and
the performance of the CI task is maximized in applications such as
restoration, classification, and semantic segmentation.
