SSVM 2007: Scale Space and Variational Methods in Computer Vision pp 533-544 | Cite as
Restoration of Images with Piecewise Space-Variant Blur
Conference paper
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 sub-regions are unknown, a segmentation procedure is performed using an evolving level set function, guided by edges and image derivatives.
Keywords
Point Spread Function Motion Blur Recovered Image Blur Kernel Geodesic Active Contour
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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