Blind Deblurring and Blind Super-Resolution Using Internal Patch Recurrence
Tomer Michaeli, Wiezmann Institute of Science, Israel

Seminar • 11 September 2014 • BM 4 233

Abstract
Small image patches tend to recur at multiple scales within high-quality natural images. This fractal behavior has been used in the past for various tasks including image compression and super-resolution. We show that this phenomenon can also be harnessed for "blind deblurring" and for "blind super-resolution", that is for removing blur or increasing resolution without a-priori knowledge of the associated blur kernel. Our key observation is that the source of the patch recurrence phenomenon is the repetitions of structures at various scales in the continuous scene. However, the way by which this continuous-domain phenomenon is manifested in the discrete image, depends on the imaging process. In particular, patches tend to repeat 'as is' in discrete images taken under ideal imaging conditions, but much less so in blurry images. These deviations from ideal patch recurrence can thus provide a cue for recovering the (unknown) blur kernel. Specifically, we show that the correct blur kernel is the one which maximizes the similarity between patches across scales of the image. Extensive experiments indicate that our approach leads to state of the art results, both in deblurring and in super-resolution.