Depth Estimation from a Single Optical Encoded Image using a Learned
  Colored-Coded Aperture
Jhon Lopez Edwin Vargas Henry Arguello
Depth estimation from a single image of a conventional camera is a
challenging task since depth cues are lost during the acquisition process.
State-of-the-art approaches improve the discrimination between different depths
by introducing a binary-coded aperture (CA) in the lens aperture that generates
different coded blur patterns at different depths. Color-coded apertures (CCA)
can also produce color misalignment in the captured image which can be utilized
to estimate disparity. Leveraging advances in deep learning, more recent works
have explored the data-driven design of a diffractive optical element (DOE) for
encoding depth information through chromatic aberrations. However, compared
with binary CA or CCA, DOEs are more expensive to fabricate and require
high-precision devices. Different from previous CCA-based approaches that
employ few basic colors, in this work we propose a CCA with a greater number of
color filters and richer spectral information to optically encode relevant
depth information in a single snapshot. Furthermore, we propose to jointly
learn the color-coded aperture (CCA) pattern and a convolutional neural network
(CNN) to retrieve depth information by using an end-to-end optimization
approach. We demonstrate through different experiments on three different data
sets that the designed color-encoding has the potential to remove depth
ambiguities and provides better depth estimates compared to state-of-the-art
approaches. Additionally, we build a low-cost prototype of our CCA using a
photographic film and validate the proposed approach in real scenarios.