Non-uniform Motion Deblurring for Bilayer Scenes

We address the problem of estimating the latent image of a static bilayer scene (consisting of a foreground and a background at different depths) from motion blurred observations captured with a handheld camera. The camera motion is considered to be composed of in-plane rotations and translations. Since the blur at an image location depends both on camera motion and depth, deblurring becomes a difficult task. We initially propose a method to estimate the transformation spread function (TSF) corresponding to one of the depth layers. The estimated TSF (which reveals the camera motion during exposure) is used to segment the scene into the foreground and background layers and determine the relative depth value. The deblurred image of the scene is finally estimated within a regularization framework by accounting for blur variations due to camera motion as well as depth.

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

[2]  Martial Hebert,et al.  Learning to Find Object Boundaries Using Motion Cues , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[3]  Jan Flusser,et al.  Space-Variant Restoration of Images Degraded by Camera Motion Blur , 2008, IEEE Transactions on Image Processing.

[4]  Kang Wang,et al.  A two-stage approach to blind spatially-varying motion deblurring , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Jia Chen,et al.  Robust dual motion deblurring , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

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

[7]  智一 吉田,et al.  Efficient Graph-Based Image Segmentationを用いた圃場図自動作成手法の検討 , 2014 .

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

[9]  Jean Ponce,et al.  Non-uniform Deblurring for Shaken Images , 2012, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[11]  Shuiwang Ji,et al.  SLEP: Sparse Learning with Efficient Projections , 2011 .

[12]  Ming-Hsuan Yang,et al.  Fast Non-uniform Deblurring using Constrained Camera Pose Subspace , 2012, BMVC.

[13]  Ankit Gupta,et al.  Single Image Deblurring Using Motion Density Functions , 2010, ECCV.

[14]  Daniel P. Huttenlocher,et al.  Efficient Belief Propagation for Early Vision , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[15]  Jian-Feng Cai,et al.  Blind motion deblurring using multiple images , 2009, J. Comput. Phys..

[16]  Jan Flusser,et al.  Multichannel blind deconvolution of spatially misaligned images , 2005, IEEE Transactions on Image Processing.

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

[18]  Seungyong Lee,et al.  Non-uniform motion deblurring for camera shakes using image registration , 2011, SIGGRAPH '11.

[19]  Bernhard Schölkopf,et al.  Fast removal of non-uniform camera shake , 2011, 2011 International Conference on Computer Vision.

[20]  Sung Yong Shin,et al.  Coded exposure imaging for projective motion deblurring , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[21]  Michael S. Brown,et al.  Richardson-Lucy Deblurring for Scenes under a Projective Motion Path , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Li Xu,et al.  Depth-aware motion deblurring , 2012, 2012 IEEE International Conference on Computational Photography (ICCP).

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

[24]  S. B. Kang,et al.  Image deblurring using inertial measurement sensors , 2010, ACM Trans. Graph..