Abstract. We propose a learning-based framework for acquiring a light
field through a coded aperture camera. Acquiring a light field is a challenging task due to the amount of data. To make the acquisition process
efficient, coded aperture cameras were successfully adopted; using these
cameras, a light field is computationally reconstructed from several images that are acquired with different aperture patterns. However, it is
still difficult to reconstruct a high-quality light field from only a few acquired images. To tackle this limitation, we formulated the entire pipeline
of light field acquisition from the perspective of an auto-encoder. This
auto-encoder was implemented as a stack of fully convolutional layers
and was trained end-to-end by using a collection of training samples. We
experimentally show that our method can successfully learn good imageacquisition and reconstruction strategies. With our method, light fields
consisting of 5 × 5 or 8 × 8 images can be successfully reconstructed
only from a few acquired images. Moreover, our method achieved superior performance over several state-of-the-art methods. We also applied
our method to a real prototype camera to show that it is capable of
capturing a real 3-D scene.