Learning Spatially Varying Pixel Exposures for Motion Deblurring

• URL: http://arxiv.org/abs/2204.07267v1
• Date: Thu, 14 Apr 2022 23:41:49 GMT
• Title: Learning Spatially Varying Pixel Exposures for Motion Deblurring
• Authors: Cindy M. Nguyen, Julien N.P. Martel, Gordon Wetzstein
• Abstract summary: We present a novel approach of leveraging spatially varying pixel exposures for motion deblurring. Our work illustrates the promising role that focal-plane sensor--processors can play in the future of computational imaging.
• Score: 49.07867902677453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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