Deblurring Face Images with Exemplars

Jinshan Pan    Zhe Hu    Zhixun Su     Ming-Hsuan Yang

Algorithm Overview


The human face is one of the most interesting subjects involved in numerous applications. Significant progress has been made towards the image deblurring problem, however, existing generic deblurring methods are not able to achieve satisfying results on blurry face images. The success of the state-of-the-art image deblurring methods stems mainly from implicit or explicit restoration of salient edges for kernel estimation. 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 object 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 posteriori (MAP) deblurring algorithm based on an exemplar dataset, without using the coarse-to-fine strategy or ad-hoc edge selections. Extensive evaluations against state-of-the-art methods demonstrate the effectiveness of the proposed algorithm for deblurring face images. We also show the extendability of our method to other specific deblurring tasks.

Quantitative Evaluation on the Proposed Face Image Dataset

(a) Results on noise-free images (b) Results on noisy images
Quantitative comparison on the proposed face image datasets

More Examples and Comparisons

Blurred image Cho and Lee Siggraph Asia 2009 Xu et al. CVPR 2013 Ours
Blurred image Cho and Lee Siggraph Asia 2009 Xu et al. CVPR 2013 Ours

Extension of the Proposed Method

Blurred image Cho and Lee Siggraph Asia 2009 Xu et al. CVPR 2013 Ours

Technical Papers, Codes, and Datasets

Jinshan Pan, Zhe Hu, Zhixun Su, and Ming-Hsuan Yang, "Deblurring Face Images with Exemplars", European Conference on Computer Vision (ECCV), 2014

    MATALB code   


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