Single Image Blind Deconvolution Using Sparse Representation and Structural Self-similarity
-
摘要: 图像盲解卷积研究当模糊核未知时，如何从模糊图像复原出原始清晰图像.由于盲解卷积是一个欠定问题，现有的盲解卷积算法都直接或间接地利用各种先验知识.本文提出了一种结合稀疏表示与结构自相似性的单幅图像盲解卷积算法，该算法将图像的稀疏性先验和结构自相似性先验作为正则化约束加入到图像盲解卷积的目标函数中，并利用图像不同尺度间的结构自相似性，将观测模糊图像的降采样图像作为稀疏表示字典的训练样本，保证清晰图像在该字典下的稀疏性.最后利用交替求解的方式估计模糊核和清晰图像.模拟和真实数据上的实验表明本文算法能够准确估计模糊核，复原清晰的图像边缘，并具有很好的鲁棒性.Abstract: Blind image deconvolution aims to recover the latent sharp image from a blurry image when the blur kernel is unknown. Since blind deconvolution is an underdetermined problem, existing methods take advantage of various prior knowledge directly or indirectly. In this article, we propose a single image blind deconvolution method based on sparse representation and structural self-similarity. In our method, we add the image sparsity prior and structural self-similarity prior to the blind deconvolution objective function as regularization constraints, and we utilize the structural self-similarity between different image scales by taking the down-sampled version of observed blurry image as the sparse representation dictionary training set so that the sparsity of the latent sharp image under this dictionary can be ensured. Finally, we estimate the blur kernel and sharp image alternately. Experimental results on both simulated and real blurry images demonstrate that the blur kernels estimated by our method are accurate and robust, and that the restored images have high visual quality with sharp edges.
-
Key words:
- Sparse representation /
- structural self-similarity /
- blind deconvolution /
- blur kernel /
- deblurring
1) 本文责任编委 杨健 -
图 2 清晰图像与模糊图像的结构相似性
Fig. 2 Structural self-similarity between sharp image and blurry image
图 7 各算法对有噪模糊图像的复原结果比较
Fig. 7 Visual comparisons with some methods on noisy blurry image
图 8 不同强度高斯噪声下各算法盲解卷积结果的均方误差
Fig. 8 Mean squared error of some methods under different noise levels
图 9 各算法在真实模糊图像(模糊核未知)上的实验结果比较
Fig. 9 Visual comparisons with some state-of-the-art methods on real-world photos with unknown kernel
图 10 各算法在真实模糊图像(模糊核未知)上的实验结果比较
Fig. 10 Visual comparisons with some state-of-the-art methods on real-world photos with unknown kernel
-
[1] Joshi N, Szeliski R, Kriegman D J. PSF estimation using sharp edge prediction. In: Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK, USA:IEEE, 2008. 1-8 http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=4587834 [2] Cho S, Lee S. Fast motion deblurring. ACM Transactions on Graphics, 2009, 28(5):Article No.145 http://portal.acm.org/citation.cfm?doid=1618452.1618491 [3] Xu L, Jia J Y. Two-phase kernel estimation for robust motion deblurring. In: Proceedings of the 11th European Conference on Computer Vision. Heraklion, Crete, Greece:Springer, 2010. 157-170 http://dl.acm.org/citation.cfm?id=1886077 [4] Levin A, Weiss Y, Durand F, Freeman W T. Understanding and evaluating blind deconvolution algorithms. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA:IEEE, 2009. 1964-1971 http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=5206815 [5] Fergus R, Singh B, Hertzmann A, Rowels S T, Freeman W T. Removing camera shake from a single photograph. ACM Transactions on Graphics, 2006, 25(3):787-794 doi: 10.1145/1141911 [6] Levin A, Weiss Y, Durand F, Freeman W T. Efficient marginal likelihood optimization in blind deconvolution. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA:IEEE, 2011. 2657-2664 http://dl.acm.org/citation.cfm?id=2192016 [7] Shan Q, Jia J Y, Agarwala A. High-quality motion deblurring from a single image. ACM Transactions on Graphics, 2008, 27(3):Article No. 73 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.218.6835 [8] Perrone D, Favaro P. Total variation blind deconvolution:the devil is in the details. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA:IEEE, 2014. 2909-2916 http://ieeexplore.ieee.org/document/6909768/ [9] Michaeli T, Irani M. Blind deblurring using internal patch recurrence. In: Proceedings of the 13th European Conference on Computer Vision. Zurich, Switzerland:Springer International Publishing, 2014. 783-798 http://www.researchgate.net/publication/291197096_Blind_Deblurring_Using_Internal_Patch_Recurrence [10] Lai W S, Ding J J, Lin Y Y, Chuang Y Y. Blur kernel estimation using normalized color-line priors. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA:IEEE, 2015. 64-72 doi: 10.1109/CVPR.2015.7298601 [11] Zhang H C, Yang J C, Zhang Y N, Huang T S. Sparse representation based blind image deblurring. In: Proceedings of the 2011 IEEE International Conference on Multimedia and Expo. Barcelona, Spain:IEEE, 2011. 1-6 http://ieeexplore.ieee.org/document/6012035/ [12] Li H S, Zhang Y N, Zhang H C, Zhu Y, Sun J Q. Blind image deblurring based on sparse prior of dictionary pair. In: Proceedings of the 21st International Conference on Pattern Recognition. Tsukuba, Japan:IEEE, 2012. 3054-3057 http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6460809 [13] 潘宗序, 禹晶, 肖创柏, 孙卫东.基于多尺度非局部约束的单幅图像超分辨率算法.自动化学报, 2014, 40(10):2233-2244 http://www.aas.net.cn/CN/abstract/abstract18498.shtmlPan Zong-Xu, Yu Jing, Xiao Chuang-Bai, Sun Wei-Dong. Single-image super-resolution algorithm based on multi-scale nonlocal regularization. Acta Automatica Sinica, 2014, 40(10):2233-2244 http://www.aas.net.cn/CN/abstract/abstract18498.shtml [14] 潘宗序, 禹晶, 胡少兴, 孙卫东.基于多尺度结构自相似性的单幅图像超分辨率算法.自动化学报, 2014, 40(4):594-603 http://www.aas.net.cn/CN/abstract/abstract18325.shtmlPan Zong-Xu, Yu Jing, Hu Shao-Xing, Sun Wei-Dong. Single image super resolution based on multi-scale structural self-similarity. Acta Automatica Sinica, 2014, 40(4):594-603 http://www.aas.net.cn/CN/abstract/abstract18325.shtml [15] 潘宗序, 禹晶, 肖创柏, 孙卫东.基于字典学习与结构自相似性的码本映射超分辨率算法.计算机辅助设计与图形学学报, 2015, 27(6):1032-1038 http://d.wanfangdata.com.cn/Periodical/jsjfzsjytxxxb201506009Pan Zong-Xu, Yu Jing, Xiao Chuang-Bai, Sun Wei-Dong. Dictionary learning and structural self-similarity-based codebook mapping for single image super resolution. Journal of Computer-Aided Design & Computer Graphics, 2015, 27(6):1032-1038 http://d.wanfangdata.com.cn/Periodical/jsjfzsjytxxxb201506009 [16] Aharon M, Elad M, Bruckstein A. rmK-SVD:an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 2006, 54(11):4311-4322 doi: 10.1109/TSP.2006.881199 [17] Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 2007, 53(12):4655-4666 doi: 10.1109/TIT.2007.909108 [18] Zoran D, Weiss Y. From learning models of natural image patches to whole image restoration. In: Proceedings of the 2011 IEEE International Conference on Computer Vision. Barcelona, Spain:IEEE, 2011. 479-486 http://dl.acm.org/citation.cfm?id=2356307 [19] Sun L B, Cho S, Wang J, Hays J. Edge-based blur kernel estimation using patch priors. In: Proceedings of the 2013 IEEE International Conference on Computational Photography. Cambridge, MA, USA:IEEE, 2013. 1-8 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6528301 [20] Glasner D, Bagon S, Irani M. Super-resolution from a single image. In: Proceedings of the 12th IEEE International Conference on Computer Vision. Kyoto, Japan:IEEE, 2009. 349-356 http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5459271 [21] Olonetsky I, Avidan S. TreeCANN-k-d tree coherence approximate nearest neighbor algorithm. In: Proceedings of the 12th European Conference on Computer Vision. Florence, Italy:Springer, 2012. 602-615 doi: 10.1007%2F978-3-642-33765-9_43 [22] Liu S G, Wang H B, Wang J, Cho S, Pan C H. Automatic blur-kernel-size estimation for motion deblurring. The Visual Computer, 2015, 31(5):733-746 doi: 10.1007/s00371-014-0998-2 [23] Perrone D, Diethelm R, Favaro P. Blind deconvolution via lower-bounded logarithmic image priors. In: Proceedings of the 10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition. Hong Kong, China:Springer International Publishing, 2015. 112-125 doi: 10.1007/978-3-319-14612-6_9 [24] Cho T S, Paris S, Horn B K P, Freeman W T. Blur kernel estimation using the radon transform. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA:IEEE, 2011. 241-248 doi: 10.1109/CVPR.2011.5995479 [25] Krishnan D, Tay T, Fergus R. Blind deconvolution using a normalized sparsity measure. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA:IEEE, 2011. 233-240 https://www.computer.org/csdl/proceedings/cvpr/2011/0394/00/05995521-abs.html -
下载:
点击查看大图
图(10) / 表(2)
计量
- 文章访问数: 2537
- HTML全文浏览量: 284
- PDF下载量: 492
- 被引次数: 0
