Automatic blur region segmentation approach using image matting

For images with partial blur such as local defocus or local motion, deconvolution with just a single point spread function surely could not restore the images correctly. Thus, restoration relying on blur region segmentation is developed widely. In this paper, we propose an automatic approach for blur region extraction. Firstly, the image is divided into patches. Then, the patches are marked by three blur features: gradient histogram span, local mean square error map, and maximum saturation. The combination of three measures is employed as the initialization of iterative image matting algorithm. At last, we separate the blurred and non-blurred region through the binarization of alpha matting map. Experiments with a set of natural images prove the advantage of our algorithm.

[1]  Nahum Kiryati,et al.  Restoration of Images with Piecewise Space-Variant Blur , 2007, SSVM.

[2]  Michael F. Cohen,et al.  An iterative optimization approach for unified image segmentation and matting , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[3]  Stefan Winkler,et al.  A no-reference perceptual blur metric , 2002, Proceedings. International Conference on Image Processing.

[4]  Huei-Yung Lin,et al.  Vehicle speed detection from a single motion blurred image , 2008, Image Vis. Comput..

[5]  Tien Tsin,et al.  Image Partial Blur Detection and Classification , 2013 .

[6]  Dani Lischinski,et al.  Spectral Matting , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Tao Xu,et al.  A simple and effective texture characterization for image segmentation , 2012, Signal Image Video Process..

[8]  Michael J. Black,et al.  Fields of Experts: a framework for learning image priors , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Daniel Freedman,et al.  An improved image graph for semi-automatic segmentation , 2012, Signal Image Video Process..

[10]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  William T. Freeman,et al.  Removing camera shake from a single photograph , 2006, SIGGRAPH 2006.

[12]  Anat Levin,et al.  Blind Motion Deblurring Using Image Statistics , 2006, NIPS.

[13]  Bobby R. Hunt,et al.  Sectioned methods for image restoration , 1978 .

[14]  Wei Zhang,et al.  Multi-Scale Blur Estimation and Edge Type Classification for Scene Analysis , 1997, International Journal of Computer Vision.

[15]  Andrew Blake,et al.  "GrabCut" , 2004, ACM Trans. Graph..

[16]  Hubert Konik,et al.  Automatic blur detection for meta-data extraction in content-based retrieval context , 2003, IS&T/SPIE Electronic Imaging.

[17]  Dani Lischinski,et al.  A Closed-Form Solution to Natural Image Matting , 2008 .

[18]  Wasfy B. Mikhael,et al.  Efficient restoration of space-variant blurs from physical optics by sectioning with modified Wiener filtering , 2003, Digit. Signal Process..

[19]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Manuel Menezes de Oliveira Neto,et al.  Fast Digital Image Inpainting , 2001, VIIP.

[21]  B. R. Hunt,et al.  Image restoration of space-variant blurs by sectioned methods , 1978 .

[22]  Sei-Wang Chen,et al.  A non-parametric blur measure based on edge analysis for image processing applications , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[23]  Jian Sun,et al.  Lazy snapping , 2004, SIGGRAPH 2004.