High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
High-quality motion deblurring from a single image
ACM SIGGRAPH 2008 papers
Toward Accurate and Fast Iris Segmentation for Iris Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH Asia 2009 papers
Ordinal Measures for Iris Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-phase kernel estimation for robust motion deblurring
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Robust and fast assessment of iris image quality
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Blind deconvolution using a normalized sparsity measure
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Real-Time Image Restoration for Iris Recognition Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Circuits and Systems for Video Technology
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Blurred iris images are inevitable during iris image acquisition due to limited depth of field and movement of subjects. The blurred iris images lose detailed texture information for accurate identity verification, so this paper proposes a novel iris image deblurring method to enhance the quality of blurred iris images. Our method makes full use of the prior information of iris images. Firstly, benefiting from the properties of iris images, a set of initialization methods for point spread function (PSF) is proposed to obtain a better start point than that of common deblurring methods. Secondly, only the most reliable iris image regions which are obtained by structure properties of iris images are used to refine the initial PSF. Finally, the more accurate PSF is used to reconstruct the clear iris texture for higher accuracy of iris recognition. Experimental results on both synthetic and real-world iris images illustrate that the proposed method is effective and efficient, and outperforms state-of-the-art iris image deblurring methods in terms of the improvement of iris recognition accuracy.