Robust deblurring based on prediction of informative structure

This study presents a robust motion deblurring method in which an adaptive prediction is used to extract the informative regions for kernel estimation. The prediction not only sharpens the blurry edges, but also adaptively predicts the large scale structure for kernel estimation. It allows to only use the alternating minimisation with a computationally efficient Gaussian prior for both the image and kernel while without employing thoughtful attention such as multi-scale scheme or kernel refinement. Extensive experiments were carried out to validate the proposed method and to compare it with some previous approaches. The experiment results demonstrated that the approach achieves, if not better than, state-of-the-art results for uniformly blurred images.

[1]  Shmuel Peleg,et al.  Two motion-blurred images are better than one , 2005, Pattern Recognit. Lett..

[2]  Jian-Feng Cai,et al.  Blind motion deblurring from a single image using sparse approximation , 2009, CVPR.

[3]  Stanley J. Reeves,et al.  Fast image restoration without boundary artifacts , 2005, IEEE Transactions on Image Processing.

[4]  Frédo Durand,et al.  Efficient marginal likelihood optimization in blind deconvolution , 2011, CVPR 2011.

[5]  Jian Sun,et al.  Progressive inter-scale and intra-scale non-blind image deconvolution , 2008, SIGGRAPH 2008.

[6]  Shree K. Nayar,et al.  Motion-based motion deblurring , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Yair Weiss,et al.  From learning models of natural image patches to whole image restoration , 2011, 2011 International Conference on Computer Vision.

[8]  Mostafa Kaveh,et al.  Blind image restoration by anisotropic regularization , 1999, IEEE Trans. Image Process..

[9]  Hui Ji,et al.  Motion blur identification from image gradients , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Li Xu,et al.  Unnatural L0 Sparse Representation for Natural Image Deblurring , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Worthy N. Martin,et al.  Image Motion Estimation From Motion Smear-A New Computational Model , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Yitzhak Yitzhaky,et al.  Direct method for restoration of motion-blurred images , 1998 .

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

[14]  Sunghyun Cho,et al.  Edge-based blur kernel estimation using patch priors , 2013, IEEE International Conference on Computational Photography (ICCP).

[15]  Richard Szeliski,et al.  PSF estimation using sharp edge prediction , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

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

[17]  Long Quan,et al.  Image deblurring with blurred/noisy image pairs , 2007, SIGGRAPH 2007.

[18]  L. Rudin,et al.  Feature-oriented image enhancement using shock filters , 1990 .

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

[20]  Bernhard Schölkopf,et al.  Recording and Playback of Camera Shake: Benchmarking Blind Deconvolution with a Real-World Database , 2012, ECCV.

[21]  Frédo Durand,et al.  Motion-invariant photography , 2008, SIGGRAPH 2008.

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

[23]  L. Lucy An iterative technique for the rectification of observed distributions , 1974 .

[24]  Ying Wu,et al.  Motion from blur , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Frédo Durand,et al.  Understanding and evaluating blind deconvolution algorithms , 2009, CVPR.

[26]  Yasuyuki Matsushita,et al.  Removing Non-Uniform Motion Blur from Images , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[27]  Wotao Yin,et al.  Compressed Sensing via Iterative Support Detection , 2009, ArXiv.

[28]  Jiaya Jia,et al.  Single Image Motion Deblurring Using Transparency , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  James Hays,et al.  Super-resolution from internet-scale scene matching , 2012, 2012 IEEE International Conference on Computational Photography (ICCP).

[30]  Ming-Hsuan Yang,et al.  Good Regions to Deblur , 2012, ECCV.

[31]  James H. Money,et al.  Total variation minimizing blind deconvolution with shock filter reference , 2008, Image Vis. Comput..

[32]  Frédo Durand,et al.  Image and depth from a conventional camera with a coded aperture , 2007, SIGGRAPH 2007.

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

[34]  Joachim Weickert,et al.  Coherence-Enhancing Shock Filters , 2003, DAGM-Symposium.

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