  Motion blur is a known issue in photography, as it limits the exposure time
while capturing moving objects. Extensive research has been carried to
compensate for it. In this work, a computational imaging approach for motion
deblurring is proposed and demonstrated. Using dynamic phase-coding in the lens
aperture during the image acquisition, the trajectory of the motion is encoded
in an intermediate optical image. This encoding embeds both the motion
direction and extent by coloring the spatial blur of each object. The color
cues serve as prior information for a blind deblurring process, implemented
using a convolutional neural network (CNN) trained to utilize such coding for
image restoration. We demonstrate the advantage of the proposed approach over
blind-deblurring with no coding and other solutions that use coded acquisition,
both in simulation and real-world experiments.
