Feature-oriented image enhancement using shock filters
SIAM Journal on Numerical Analysis
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Image Motion Estimation From Motion Smear-A New Computational Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Image and depth from a conventional camera with a coded aperture
ACM SIGGRAPH 2007 papers
Edge-preserving decompositions for multi-scale tone and detail manipulation
ACM SIGGRAPH 2008 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
Enhancing photographs using content-specific image priors
Enhancing photographs using content-specific image priors
ACM SIGGRAPH Asia 2009 papers
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
SIAM Journal on Imaging Sciences
Image deblurring using inertial measurement sensors
ACM SIGGRAPH 2010 papers
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
Image smoothing via L0 gradient minimization
Proceedings of the 2011 SIGGRAPH Asia Conference
Blind deconvolution using a normalized sparsity measure
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Efficient marginal likelihood optimization in blind deconvolution
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Blur kernel estimation using the radon transform
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Total variation blind deconvolution
IEEE Transactions on Image Processing
Motion-aware noise filtering for deblurring of noisy and blurry images
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
A two-stage approach to blind spatially-varying motion deblurring
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Handling outliers in non-blind image deconvolution
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Fast removal of non-uniform camera shake
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Structure extraction from texture via relative total variation
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Blur-Kernel estimation from spectral irregularities
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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Blind image deblurring algorithms have been improving steadily in the past years. Most state-of-the-art algorithms, however, still cannot perform perfectly in challenging cases, especially in large blur setting. In this paper, we focus on how to estimate a good blur kernel from a single blurred image based on the image structure. We found that image details caused by blur could adversely affect the kernel estimation, especially when the blur kernel is large. One effective way to remove these details is to apply image denoising model based on the total variation (TV). First, we developed a novel method for computing image structures based on the TV model, such that the structures undermining the kernel estimation will be removed. Second, we applied a gradient selection method to mitigate the possible adverse effect of salient edges and improve the robustness of kernel estimation. Third, we proposed a novel kernel estimation method, which is capable of removing noise and preserving the continuity in the kernel. Finally, we developed an adaptive weighted spatial prior to preserve sharp edges in latent image restoration. Extensive experiments testify to the effectiveness of our method on various kinds of challenging examples.