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Computationally removing the motion blur introduced by camera shake or objectmotion in a captured image remains a challenging task in computationalphotography. Deblurring methods are often limited by the fixed global exposuretime of the image capture process. The post-processing algorithm either mustdeblur a longer exposure that contains relatively little noise or denoise ashort exposure that intentionally removes the opportunity for blur at the costof increased noise. We present a novel approach of leveraging spatially varyingpixel exposures for motion deblurring using next-generation focal-planesensor--processors along with an end-to-end design of these exposures and amachine learning--based motion-deblurring framework. We demonstrate insimulation and a physical prototype that learned spatially varying pixelexposures (L-SVPE) can successfully deblur scenes while recovering highfrequency detail. Our work illustrates the promising role that focal-planesensor--processors can play in the future of computational imaging.

Learning Spatially Varying Pixel Exposures for Motion Deblurring