Abstract

The human face is one of the most interesting sub jects involved in numerous applications. Significant progress has been made towards the image deblurring problem, however, existing generic deblur- ring methods are not able to achieve satisfying results on blurry face im- ages. The success of the state-of-the-art image deblurring methods stems mainly from implicit or explicit restoration of salient edges for kernel es- timation. When there is not much texture in the blurry image (e.g., face images), existing methods are less effective as only few edges can be used for kernel estimation. Moreover, recent methods are usually jeopardized by selecting ambiguous edges, which are imaged from the same edge of the ob ject after blur, for kernel estimation due to local edge selection strategies. In this paper, we address these problems of deblurring face images by exploiting facial structures. We propose a maximum a poste- riori (MAP) deblurring algorithm based on an exemplar dataset, with- out using the coarse-to-fine strategy or ad-hoc edge selections. Extensive evaluations against state-of-the-art methods demonstrate the effective- ness of the proposed algorithm for deblurring face images. We also show the extendability of our method to other specific deblurring tasks.