Abstract
Blind motion deblurring from a single image is a highly under-constrained
problem with many degenerate solutions. A good approximation of the
intrinsic image can therefore only be obtained with the help of prior
information in the form of (often non-convex) regularization terms for
both the intrinsic image and the kernel. While the best choice of
image priors is still a topic of ongoing investigation, this research
is made more complicated by the fact that historically each new prior
requires the development of a custom optimization method. In this paper,
we develop a stochastic optimization method for blind
deconvolution. Since this stochastic solver does not require the
explicit computation of the gradient of the objective function and uses only efficient local
evaluation of the objective, new priors can be implemented and tested
very quickly. We demonstrate that this framework, in combination with
different image priors produces results with PSNR values that match or
exceed the results obtained by much more complex state-of-the-art
blind motion deblurring algorithms.
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