Motion deblurring using the similarity of the multiscales

Abstract Motion blur exists widely when the image is captured using a hand-held camera, especially when the exposure time is long. Many motion deblurring methods have been proposed to estimate a satisfying latent image; however, these always exist some artifacts in the deblurring result. In this paper, via introducing the similarity of multiscales, a blind image deblurring method is proposed. Considering similarity of the latent image between different scales, it is used as a prior to constrain the estimated latent image similar as pre-scale to reduce artifacts which result from deconvolution. Experiments indicate that the proposed method could obtain better performance than the state-of-the-art methods, and could obtain satisfying deblurring result with fewer artifacts.

[1]  T. S. Cho,et al.  Motion blur removal with orthogonal parabolic exposures , 2010, 2010 IEEE International Conference on Computational Photography (ICCP).

[2]  Aggelos K. Katsaggelos,et al.  Bayesian Blind Deconvolution with General Sparse Image Priors , 2012, ECCV.

[3]  Feihong Yu,et al.  Biconjugate gradient stabilized method in image deconvolution of a wavefront coding system , 2013 .

[4]  Houzhang Fang,et al.  Blind image deconvolution with spatially adaptive total variation regularization. , 2012, Optics letters.

[5]  Michal Šorel,et al.  Platform motion blur image restoration system. , 2012, Applied optics.

[6]  Nima Nikzad,et al.  Platform Motion Blur Image Restoration System , 2012 .

[7]  Anat Levin,et al.  User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Stephen Lin,et al.  Image/video deblurring using a hybrid camera , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Frédo Durand,et al.  Understanding Blind Deconvolution Algorithms , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Rob Fergus,et al.  Blind deconvolution using a normalized sparsity measure , 2011, CVPR 2011.

[11]  Li Xu,et al.  Two-Phase Kernel Estimation for Robust Motion Deblurring , 2010, ECCV.

[12]  Bobby Bodenheimer,et al.  Synthesis and evaluation of linear motion transitions , 2008, TOGS.

[13]  Wei Zhang,et al.  Satellite image deconvolution based on nonlocal means. , 2010, Applied optics.

[14]  Jiaya Jia,et al.  High-quality motion deblurring from a single image , 2008, SIGGRAPH 2008.

[15]  David A. Forsyth,et al.  Generalizing motion edits with Gaussian processes , 2009, ACM Trans. Graph..

[16]  Weimin Jin,et al.  Real-time image deblurring by optoelectronic hybrid processing. , 2011, Applied optics.

[17]  Sunghyun Cho,et al.  Fast motion deblurring , 2009, SIGGRAPH 2009.

[18]  Thomas S. Huang,et al.  Close the loop: Joint blind image restoration and recognition with sparse representation prior , 2011, 2011 International Conference on Computer Vision.

[19]  Chun Chen,et al.  Video-based non-uniform object motion blur estimation and deblurring , 2012, Neurocomputing.

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