  Defocus blur arises in images that are captured with a shallow depth of field
due to the use of a wide aperture. Correcting defocus blur is challenging
because the blur is spatially varying and difficult to estimate. We propose an
effective defocus deblurring method that exploits data available on dual-pixel
(DP) sensors found on most modern cameras. DP sensors are used to assist a
camera's auto-focus by capturing two sub-aperture views of the scene in a
single image shot. The two sub-aperture images are used to calculate the
appropriate lens position to focus on a particular scene region and are
discarded afterwards. We introduce a deep neural network (DNN) architecture
that uses these discarded sub-aperture images to reduce defocus blur. A key
contribution of our effort is a carefully captured dataset of 500 scenes (2000
images) where each scene has: (i) an image with defocus blur captured at a
large aperture; (ii) the two associated DP sub-aperture views; and (iii) the
corresponding all-in-focus image captured with a small aperture. Our proposed
DNN produces results that are significantly better than conventional single
image methods in terms of both quantitative and perceptual metrics -- all from
data that is already available on the camera but ignored. The dataset, code,
and trained models are available at
https://github.com/Abdullah-Abuolaim/defocus-deblurring-dual-pixel.
