In this thesis, algorithms for blind and non-blind motion deblurring
of digital images are proposed. The non-blind algorithm is based on a convex program
consisting of a data fitting term and a sparsity-promoting regularization term.
The data fitting term is the squared l_2 norm of the residual between the blurred image
and the latent image convolved with a known blur kernel.
The regularization term
is the l_1 norm of the latent image under a wavelet frame (framelet) decomposition.
This convex program is solved with the first-order primal-dual algorithm proposed by Chambolle and Pock. The proposed blind deblurring algorithm
is based on the work of Cai, Ji, Liu, and Shen.
It works by embedding the proposed non-blind algorithm in an alternating minimization scheme
and imposing additional constraints in order
to deal with the challenging non-convex nature of the blind deblurring problem.
Numerical experiments are performed on artificially and naturally blurred images,
and both proposed algorithms are found to be competitive with recent deblurring methods. / Graduate / 0544 / tdanniels@gmail.com
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/5107 |
| Date | 23 December 2013 |
| Creators | Danniels, Travis |
| Contributors | Gulliver, T. Aaron |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
Page generated in 0.0047 seconds