Motion Deblurring using Spatiotemporal Phase Aperture Coding
This work addresses motion blur issues for photographers and imaging applications, offering a novel hybrid solution that combines optical coding with neural networks.
The paper tackles motion blur in photography by proposing a computational imaging approach that uses dynamic phase-coding in the lens aperture to encode motion trajectory into color cues, enabling a CNN-based blind deblurring process that outperforms existing methods in simulations and real-world experiments.
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.