[논문]Dynamic Video Deblurring Using a Locally Adaptive Blur Model

Kim, Tae Hyun; Nah, Seungjun; Lee, Kyoung Mu

doi:10.1109/tpami.2017.2761348

Dynamic Video Deblurring Using a Locally Adaptive Blur Model

IEEE transactions on pattern analysis and machine intelligence, v.40 no.10, 2018년, pp.2374 - 2387

Kim, Tae Hyun (Department of Electrical Engineering and Computer Science, Seoul National University, Seoul, South Korea) , Nah, Seungjun (Department of Electrical Engineering and Computer Science, Seoul National University, Seoul, South Korea) , Lee, Kyoung Mu (Department of Electrical Engineering and Computer Science, Seoul National University, Seoul, South Korea)

Abstract ▼ AI-Helper

State-of-the-art video deblurring methods cannot handle blurry videos recorded in dynamic scenes since they are built under a strong assumption that the captured scenes are static. Contrary to the existing methods, we propose a new video deblurring algorithm that can deal with general blurs inherent in dynamic scenes. To handle general and locally varying blurs caused by various sources, such as moving objects, camera shake, depth variation, and defocus, we estimate pixel-wise varying non-uniform blur kernels. We infer bidirectional optical flows to handle motion blurs, and also estimate Gaussian blur maps to remove optical blur from defocus. Therefore, we propose a single energy model that jointly estimates optical flows, defocus blur maps and latent frames. We also provide a framework and efficient solvers to minimize the proposed energy model. By optimizing the energy model, we achieve significant improvements in removing general blurs, estimating optical flows, and extending depth-of-field in blurry frames. Moreover, in this work, to evaluate the performance of non-uniform deblurring methods objectively, we have constructed a new realistic dataset with ground truths. In addition, extensive experimental results on publicly available challenging videos demonstrate that the proposed method produces qualitatively superior performance than the state-of-the-art methods which often fail in either deblurring or optical flow estimation.

참고문헌 (47)

Chambolle, Antonin, Pock, Thomas. A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging. Journal of mathematical imaging and vision, vol.40, no.1, 120-145.

상세보기
10.1109/ICCV.2013.415
Proc 22nd Int Conf Neural Inf Process Syst Fast image deconvolution using hyper-laplacian priors krishnan 2009 1033
10.1109/CVPR.2015.7298665
10.1109/CVPR.2012.6247871
Proc IEEE Conf Comput Vis Pattern Recognit Motion from blur dai 2008 1
10.1109/ICCV.2013.392
10.1109/CVPR.2014.370
Levin, A., Weiss, Y.. User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior. IEEE transactions on pattern analysis and machine intelligence, vol.29, no.9, 1647-1654.

상세보기
10.1109/CVPR.2011.5995521
Yu-Wing Tai, Ping Tan, Brown, M. S.. Richardson-Lucy Deblurring for Scenes under a Projective Motion Path. IEEE transactions on pattern analysis and machine intelligence, vol.33, no.8, 1603-1618.

상세보기
Proc Eur Conf Comput Vis Non-local total generalized variation for optical flow estimation ranftl 2014 439
Cho, Sunghyun, Cho, Hojin, Tai, Yu‐Wing, Lee, Seungyong. Registration Based Non‐uniform Motion Deblurring. Computer graphics forum : journal of the European Association for Computer Graphics, vol.31, no.7, 2183-2192.

상세보기
10.1109/CVPR.2013.148
10.1109/CVPR.2013.42
10.1109/ICCV.2007.4408904
10.1109/ICCV.2007.4409009
Wulff, J., Black, M.J.. Modeling Blurred Video with Layers. Lecture notes in computer science, vol.8694, 236-252.

상세보기
10.1109/CVPR.2014.348
Matsushita, Y., Ofek, E., Weina Ge, Xiaoou Tang, Heung-Yeung Shum. Full-frame video stabilization with motion inpainting. IEEE transactions on pattern analysis and machine intelligence, vol.28, no.7, 1150-1163.

상세보기
Cho, Sunghyun, Wang, Jue, Lee, Seungyong. Video deblurring for hand-held cameras using patch-based synthesis. ACM transactions on graphics, vol.31, no.4, 1-9.

상세보기
Schuler, Christian J., Hirsch, Michael, Harmeling, Stefan, Scholkopf, Bernhard. Learning to Deblur. IEEE transactions on pattern analysis and machine intelligence, vol.38, no.7, 1439-1451.

상세보기
Proc Eur Conf Comput Vis Single image deblurring using motion density functions gupta 2010 171
Proc Advances Neural Inf Process Syst Deep convolutional neural network for image deconvolution xu 2014 1790
Pan, Jinshan, Hu, Zhe, Su, Zhixun, Yang, Ming-Hsuan. $L_0$ -Regularized Intensity and Gradient Prior for Deblurring Text Images and Beyond. IEEE transactions on pattern analysis and machine intelligence, vol.39, no.2, 342-355.

상세보기
ACM Trans Graph High-quality motion deblurring from a single image shan 2008 10.1145/1360612.1360672 27

상세보기
10.1109/CVPR.2015.7298677
10.1109/ICCV.2011.6126276
Cai, Jian-Feng, Ji, Hui, Liu, Chaoqiang, Shen, Zuowei. Blind motion deblurring using multiple images. Journal of computational physics, vol.228, no.14, 5057-5071.

상세보기
Whyte, Oliver, Sivic, Josef, Zisserman, Andrew, Ponce, Jean. Non-uniform Deblurring for Shaken Images. International journal of computer vision, vol.98, no.2, 168-186.

상세보기
ACM Trans Graph Fast motion deblurring cho 2009 10.1145/1618452.1618491 28

상세보기
10.1109/CVPR.2010.5539938
Fergus, Rob, Singh, Barun, Hertzmann, Aaron, Roweis, Sam T., Freeman, William T.. Removing camera shake from a single photograph. ACM transactions on graphics, vol.25, no.3, 787-794.

상세보기
Proc Eur Conf Comput Vis A naturalistic open source movie for optical flow evaluation butler 2012 611
10.1109/CVPR.2015.7299181
IEEE Trans Pattern Anal Mach Intell Motion detail preserving optical flow estimation xu 2012 10.1109/TPAMI.2011.236 34 1744

상세보기
10.1109/ICCPHOT.2011.5753120
Zach, C., Pock, T., Bischof, H.. A Duality Based Approach for Realtime TV-L¹ Optical Flow. Lecture notes in computer science, vol.4713, 214-223.

상세보기
Bae, Soonmin, Durand, Frédo. Defocus Magnification. Computer graphics forum : journal of the European Association for Computer Graphics, vol.26, no.3, 571-579.

상세보기
Statistical and Geometrical Approaches to Visual Motion Analysis An improved algorithm for TV-L 1 optical flow wedel 2009 10.1007/978-3-642-03061-1_2 23

상세보기
Zhuo, S., Sim, T.. Defocus map estimation from a single image. Pattern recognition, vol.44, no.9, 1852-1858.

상세보기
10.1109/CVPR.2010.5539945
Xiang Zhu, Cohen, Scott, Schiller, Stephen, Milanfar, Peyman. Estimating Spatially Varying Defocus Blur From A Single Image. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol.22, no.12, 4879-4891.

상세보기
10.1109/CVPR.2009.5206546
Proc Eur Conf Comput Vis Recording and playback of camera shake: Benchmarking blind deconvolution with a real-world database köhler 2012 27
10.1109/CVPR.2011.5995372
10.1109/CVPR.2009.5206815

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Dynamic Video Deblurring Using a Locally Adaptive Blur Model

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Dynamic Video Deblurring Using a Locally Adaptive Blur Model

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