Image deblurring via self-similarity and via sparsity
Dr. Yifei Lou
School of Electrical and Computer Engineering
Georgia Institute of Technology
ABSTRACT
In this talk, I will present two deblurring methods, one exploits the
spatial interactions in images, i.e. the self-similarity; and the
other explicitly takes into account the sparse characteristics of
natural images and does not entail solving a numerically
ill-conditioned backward-diffusion.
In particular, the self-similarity is defined by a weight function,
which induces two types of regularization in a nonlocal fashion.
Furthermore, we get superior results using preprocessed data as input
for the weighted functionals.
The second part of the talk is based on the observation that the
sparse coefficients that encode a given image with respect to an
over-complete basis are the same that encode a blurred version of the
image with respect to a modified basis. Following an
``analysis-by-synthesis'' approach, an explicit generative model is
used to compute a sparse representation of the blurred image, and the
coefficients of which are used to combine elements of the original
basis to yield a restored image.
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