  Computationally removing the motion blur introduced by camera shake or object
motion in a captured image remains a challenging task in computational
photography. Deblurring methods are often limited by the fixed global exposure
time of the image capture process. The post-processing algorithm either must
deblur a longer exposure that contains relatively little noise or denoise a
short exposure that intentionally removes the opportunity for blur at the cost
of increased noise. We present a novel approach of leveraging spatially varying
pixel exposures for motion deblurring using next-generation focal-plane
sensor--processors along with an end-to-end design of these exposures and a
machine learning--based motion-deblurring framework. We demonstrate in
simulation and a physical prototype that learned spatially varying pixel
exposures (L-SVPE) can successfully deblur scenes while recovering high
frequency detail. Our work illustrates the promising role that focal-plane
sensor--processors can play in the future of computational imaging.
