Design and Estimation of Coded Exposure Point Spread Functions

We address the problem of motion deblurring using coded exposure. This approach allows for accurate estimation of a sharp latent image via well-posed deconvolution and avoids lost image content that cannot be recovered from images acquired with a traditional shutter. Previous work in this area has u...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2012-10, Vol.34 (10), p.2071-2077
Main Authors: McCloskey, Scott, Ding, Yuanyuan, Yu, Jingyi
Format: Article
Language:English
Subjects:
Acceleration
Applied sciences
Artificial intelligence
blur estimation
Coded exposure
computational photography
Computer science
control theory
systems
Correlation
Estimation
Exact sciences and technology
Image edge detection
Image reconstruction
motion deblurring
Noise measurement
Pattern recognition. Digital image processing. Computational geometry
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Summary:We address the problem of motion deblurring using coded exposure. This approach allows for accurate estimation of a sharp latent image via well-posed deconvolution and avoids lost image content that cannot be recovered from images acquired with a traditional shutter. Previous work in this area has used either manual user input or alpha matting approaches to estimate the coded exposure Point Spread Function (PSF) from the captured image. In order to automate deblurring and to avoid the limitations of matting approaches, we propose a Fourier-domain statistical approach to coded exposure PSF estimation that allows us to estimate the latent image in cases of constant velocity, constant acceleration, and harmonic motion. We further demonstrate that previously used criteria to choose a coded exposure PSF do not produce one with optimal reconstruction error, and that an additional 30 percent reduction in Root Mean Squared Error (RMSE) of the latent image estimate can be achieved by incorporating natural image statistics.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2012.108