Coded Exposure Deblurring: Optimized Codes for PSF Estimation and Invertibility

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 [17] 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...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Amit K. Agrawal, Yi Xu
• Keywords: Coded Exposure, Computer Vision, CVPR 2009, Deconvolution Algorithms, Motion Deblurring