Class-Specific Image Deblurring

In image deblurring, a fundamental problem is that the blur kernel suppresses a number of spatial frequencies that are difficult to recover reliably. In this paper, we explore the potential of a class-specific image prior for recovering spatial frequencies attenuated by the blurring process. Specifically, we devise a prior based on the class-specific subspace of image intensity responses to band-pass filters. We learn that the aggregation of these subspaces across all frequency bands serves as a good class-specific prior for the restoration of frequencies that cannot be recovered with generic image priors. In an extensive validation, our method, equipped with the above prior, yields greater image quality than many state-of-the-art methods by up to 5 dB in terms of image PSNR, across various image categories including portraits, cars, cats, pedestrians and household objects.

[1]  Cordelia Schmid,et al.  Bandit Algorithms for Tree Search , 2007, UAI.

[2]  Ming-Hsuan Yang,et al.  Deblurring Face Images with Exemplars , 2014, ECCV.

[3]  Edward H. Adelson,et al.  Personal photo enhancement using example images , 2010, TOGS.

[4]  Antonio Torralba,et al.  Statistics of natural image categories , 2003, Network.

[5]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[6]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

[8]  Rob Fergus,et al.  Fast Image Deconvolution using Hyper-Laplacian Priors , 2009, NIPS.

[9]  Frédo Durand,et al.  Image and depth from a conventional camera with a coded aperture , 2007, ACM Trans. Graph..

[10]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[11]  Andrew Zisserman,et al.  Deblurring shaken and partially saturated images , 2011, ICCV Workshops.

[12]  Frédo Durand,et al.  Understanding and evaluating blind deconvolution algorithms , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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

[14]  Seungyong Lee,et al.  Fast motion deblurring , 2009, ACM Trans. Graph..

[15]  Arnold W. M. Smeulders,et al.  c ○ 2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands. A Six-Stimulus Theory for Stochastic Texture , 2002 .

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

[17]  Sundaresh Ram,et al.  Removing Camera Shake from a Single Photograph , 2009 .

[18]  Stephen P. Boyd,et al.  An Interior-Point Method for Large-Scale $\ell_1$-Regularized Least Squares , 2007, IEEE Journal of Selected Topics in Signal Processing.

[19]  Jonathan Krause,et al.  3D Object Representations for Fine-Grained Categorization , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[20]  Dani Lischinski,et al.  Deblurring by Example Using Dense Correspondence , 2013, 2013 IEEE International Conference on Computer Vision.

[21]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Ming-Hsuan Yang,et al.  Deblurring Text Images via L0-Regularized Intensity and Gradient Prior , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Hui Ma,et al.  Image Deblurring with Blurred / Noisy Image Pairs , 2013 .

[24]  Sunghyun Cho,et al.  Good Image Priors for Non-blind Deconvolution - Generic vs. Specific , 2014, ECCV.

[25]  Weiwei Zhang,et al.  Cat Head Detection - How to Effectively Exploit Shape and Texture Features , 2008, ECCV.

[26]  Li Xu,et al.  Discriminative Blur Detection Features , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

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

[30]  Jian-Feng Cai,et al.  Framelet-Based Blind Motion Deblurring From a Single Image , 2012, IEEE Transactions on Image Processing.

[31]  Jiaya Jia,et al.  High-quality motion deblurring from a single image , 2008, ACM Trans. Graph..

[32]  Sylvain Paris,et al.  Handling Noise in Single Image Deblurring Using Directional Filters , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[33]  Eero P. Simoncelli,et al.  Modeling Multiscale Subbands of Photographic Images with Fields of Gaussian Scale Mixtures , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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