Feature-oriented image enhancement using shock filters
SIAM Journal on Numerical Analysis
Motion-Based Motion Deblurring
IEEE Transactions on Pattern Analysis and Machine Intelligence
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
High-quality motion deblurring from a single image
ACM SIGGRAPH 2008 papers
Progressive inter-scale and intra-scale non-blind image deconvolution
ACM SIGGRAPH 2008 papers
ACM SIGGRAPH Asia 2009 papers
Image deblurring using inertial measurement sensors
ACM SIGGRAPH 2010 papers
Correction of Spatially Varying Image and Video Motion Blur Using a Hybrid Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence
Space-variant deblurring using one blurred and one underexposed image
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Two-phase kernel estimation for robust motion deblurring
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Single image deblurring using motion density functions
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Richardson-Lucy Deblurring for Scenes under a Projective Motion Path
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient marginal likelihood optimization in blind deconvolution
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Total variation blind deconvolution
IEEE Transactions on Image Processing
Analyzing Image Deblurring Through Three Paradigms
IEEE Transactions on Image Processing
Fast removal of non-uniform camera shake
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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In this paper we address the problem of estimating latent sharp image and unknown blur kernel from a single motion-blurred image. The blur results from camera shake and is spatially variant. Meanwhile, the blur kernel of motion has three degrees of freedom, i.e., translations and in-plane rotation. In order to solve this problem, we first analyzed the homography blur model for the non-uniform camera-shake blur. We simplified the model to 3-dimensional camera motion which can be accelerated by exploiting the fast Fourier transform to process subsequent image deconvolution. We then proposed an effective method to handle the blind image-deblurring problem by the image decomposition, which does not need to segment the image into local subregions under the assumption of spatially invariant blur. Experimental results on both synthetic and real blurred images show that the presented approach can successfully remove various kinds of blur.