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Summary: Coded Exposure Deblurring: Optimized Codes for
PSF Estimation and Invertibility
Amit Agrawal Yi Xu
Mitsubishi Electric Research Labs (MERL)
201 Broadway, Cambridge, MA, USA
[agrawal@merl.com,xu43@cs.purdue.edu]
Abstract
We consider the problem of single image object motion
deblurring from a static camera. It is well-known that de-
blurring of moving objects using a traditional camera is ill-
posed, due to the loss of high spatial frequencies in the cap-
tured blurred image. A coded exposure camera [17] modu-
lates the integration pattern of light by opening and closing
the shutter within the exposure time using a binary code.
The code is chosen to make the resulting point spread func-
tion (PSF) invertible, for best deconvolution performance.
However, for a successful deconvolution algorithm, PSF
estimation is as important as PSF invertibility. We show
that PSF estimation is easier if the resulting motion blur
is smooth and the optimal code for PSF invertibility could
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