Total variation minimizing blind deconvolution with shock filter reference
Image and Vision Computing
Progressive inter-scale and intra-scale non-blind image deconvolution
ACM SIGGRAPH 2008 papers
Blind Deconvolution Models Regularized by Fractional Powers of the Laplacian
Journal of Mathematical Imaging and Vision
An Improved FoE Model for Image Deblurring
International Journal of Computer Vision
Journal of Mathematical Imaging and Vision
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Nonlocal Variational Image Deblurring Models in the Presence of Gaussian or Impulse Noise
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Restoration of images with piecewise space-variant blur
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
The beltrami-mumford-shah functional
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Two-dimensional bar code out-of-focus deblurring via the Increment Constrained Least Squares filter
Pattern Recognition Letters
MRF-Based blind image deconvolution
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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Image restoration and segmentation are both classical problems, that are known to be difficult and have attracted major research efforts. This paper shows that the two problems are tightly coupled and can be successfully solved together. Mutual support of image restoration and segmentation processes within a joint variational framework is theoretically motivated, and validated by successful experimental results. The proposed variational method integrates semi-blind image deconvolution (parametric blur-kernel), and Mumford-Shah segmentation. The functional is formulated using the Γ-convergence approximation and is iteratively optimized via the alternate minimization method. While the major novelty of this work is in the unified treatment of the semi-blind restoration and segmentation problems, the important special case of known blur is also considered and promising results are obtained.