Restoration of images with piecewise space-variant blur

Authors:
Leah Bar;Nir Sochen;Nahum Kiryati
Affiliations:
Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN;Dept. of Applied Mathematics, Tel Aviv University, Tel Aviv, Israel;School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
Venue:
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Year:
2007

Citing 14
Cited 0

Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations

Journal of Computational Physics
Scale-Space and Edge Detection Using Anisotropic Diffusion

IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Geodesic Active Contours

International Journal of Computer Vision
Restoring Images Degraded by Spatially Variant Blur

SIAM Journal on Scientific Computing
Separation of Transparent Layers using Focus

International Journal of Computer Vision
A Variational Approach to Shape from Defocus

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Geometric Level Set Methods in Imaging,Vision,and Graphics

Geometric Level Set Methods in Imaging,Vision,and Graphics
A Geometric Approach to Shape from Defocus

IEEE Transactions on Pattern Analysis and Machine Intelligence
Variational deblurring of images with uncertain and spatially variant blurs

PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Fast, robust total variation-based reconstruction of noisy, blurred images

IEEE Transactions on Image Processing
Blind image restoration by anisotropic regularization

IEEE Transactions on Image Processing
Active contours without edges

IEEE Transactions on Image Processing
Semi-blind image restoration via Mumford-Shah regularization

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

We address the problem of space-variant image deblurring, where different parts of the image are blurred by different blur kernels. Assuming a region-wise space variant point spread function, we first solve the problem for the case of known blur kernels and known boundaries between the different blur regions in the image. We then generalize the method to the challenging case of unknown boundaries between the blur domains. Using variational and level set techniques, the image is processed globally. The space-variant deconvolution process is stabilized by a unified common regularizer, thus preserving discontinuities between the differently restored image regions. In the case where the blurred subregions are unknown, a segmentation procedure is performed using an evolving level set function, guided by edges and image derivatives.