Blind Deblurring Using Internal Patch Recurrence^*

Tomer Michaeli and Michal Irani

Abstract. 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.

Keywords: Blind deblurring, blind deconvolution, blur kernel estimation, internal patch recurrence, fractal property, statistics of natural images

Electronic Supplementary Material:

• Electronic Supplementary Material (PDF 339 KB)

LNCS 8691, p. 783 ff.

Full article in PDF | BibTeX

lncs@springer.com
© Springer International Publishing Switzerland 2014