TY - JOUR
T1 - Motion deblurring using spatiotemporal phase aperture coding
AU - Elmalem, Shay
AU - Giryes, Raja
AU - Marom, Emanuel
N1 - Publisher Copyright:
© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
PY - 2020/10
Y1 - 2020/10
N2 - Motion-related image blur is a known issue in photography. In practice, it limits the exposure time while capturing moving objects; thus, achieving proper exposure is difficult. Extensive research has been carried out to compensate for it, to allow increased light throughput without motion artifacts. In this work, a joint optical-digital processing method for motion deblurring is proposed and demonstrated. Using dynamic phase coding in the lens aperture during the image acquisition, the motion trajectory is encoded in an intermediate optical image. This coding embeds cues for both the motion direction and extent by coloring the spatial blur of each object. These color cues serve as guidance for a digital deblurring process, implemented using a convolutional neural network (CNN) trained to utilize such coding for image restoration. Particularly, unlike previous optical coding solutions, our strategy encodes cues with no limitation on the motion direction, and without sacrificing light efficiency. We demonstrate the advantage of the proposed approach over blind deblurring methods with no optical coding, as well as over other solutions that use coded acquisition, in both simulation and real-world experiments.
AB - Motion-related image blur is a known issue in photography. In practice, it limits the exposure time while capturing moving objects; thus, achieving proper exposure is difficult. Extensive research has been carried out to compensate for it, to allow increased light throughput without motion artifacts. In this work, a joint optical-digital processing method for motion deblurring is proposed and demonstrated. Using dynamic phase coding in the lens aperture during the image acquisition, the motion trajectory is encoded in an intermediate optical image. This coding embeds cues for both the motion direction and extent by coloring the spatial blur of each object. These color cues serve as guidance for a digital deblurring process, implemented using a convolutional neural network (CNN) trained to utilize such coding for image restoration. Particularly, unlike previous optical coding solutions, our strategy encodes cues with no limitation on the motion direction, and without sacrificing light efficiency. We demonstrate the advantage of the proposed approach over blind deblurring methods with no optical coding, as well as over other solutions that use coded acquisition, in both simulation and real-world experiments.
UR - http://www.scopus.com/inward/record.url?scp=85092295240&partnerID=8YFLogxK
U2 - 10.1364/OPTICA.399533
DO - 10.1364/OPTICA.399533
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AN - SCOPUS:85092295240
SN - 2334-2536
VL - 7
SP - 1332
EP - 1340
JO - Optica
JF - Optica
IS - 10
ER -