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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Blind Deblurring Using Internal Patch Recurrence
verfasst von : Tomer Michaeli, Michal Irani
Erschienen in: Computer Vision – ECCV 2014
Verlag: Springer International Publishing
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Recurrence of small image patches across different scales of a natural image has been previously used for solving ill-posed problems (e.g. super- resolution from a single image). In this paper we show how this multi-scale property can also be used for “blind-deblurring”, namely, removal of an unknown blur from a blurry image. While patches repeat ‘as is’ across scales in a
sharp
natural image, this cross-scale recurrence significantly diminishes in blurry images. We exploit these
deviations from ideal patch recurrence
as a cue for recovering the underlying (unknown) blur kernel. More specifically, we look for the blur kernel
k
, such that if its effect is
“undone”
(if the blurry image is deconvolved with
k
), the patch similarity across scales of the image will be maximized. We report extensive experimental evaluations, which indicate that our approach compares favorably to state-of-the-art blind deblurring methods, and in particular, is more robust than them.