Coded Exposure Deblurring: Optimized Codes for
PSF Estimation and Invertibility


Amit Agrawal and Yi Xu
CVPR 2009


We consider the problem of single image object motion deblurring from a static camera. It is well-known that deblurring of moving objects using a traditional camera is ill posed, due to the loss of high spatial frequencies in the captured blurred image. A coded exposure camera modulates 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 function (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 worsen PSF estimation, since it leads to non-smooth blur.

We show that both criterions of PSF invertibility and PSF estimation can be simultaneously met, albeit with a slight increase in the deconvolution noise. We propose design rules for a code to have good PSF estimation capability and outline two search criteria for finding the optimal code for a given length. We present theoretical analysis comparing the performance of the proposed code with the code optimized solely for PSF invertibility. We also show how to easily implement coded exposure on a consumer grade machine vision camera with no additional hardware. Real experimental results demonstrate the effectiveness of the proposed codes for motion deblurring.

Paper (Preprint)

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Related Papers in Motion/Focus Deblurring

SIGGRAPH 2006      Coded exposure for motion deblurring
SIGGRAPH 2007      Coded aperture and a new theory of light field capture
SIGGRAPH 2009      Invertible Motion Blur in Video

CVPR 2007              Simultaneous motion deblurring and super-resolution
CVPR 2009              Optimal Single Image Capture for Motion Deblurring

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