Stochastic Blind Motion Deblurring

Lei Xiao, James Gregson, Felix Heide, Wolfgang Heidrich

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)3071-3085
Number of pages15
JournalIEEE Transactions on Image Processing
Issue number10
StatePublished - May 13 2015


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