Multigrid Computational Methods with Applications to Image Problems
Multigrid computational techniques are known to be amongst the most efficient methods for the numerical solution of several classes of problems. Originally developed for partial differential equations in the 1970's, multigrid (more generally, multi-level) algorithms are currently employed, in academia and industry, as efficient solvers for an ever increasing variety of linear and nonlinear problems involving many variables.
In this talk, the multigrid approach will be introduced with a simple example, followed by a presentation of a few of our recent developments and applications for problems in image analysis and processing, as time allows. Our recent and ongoing research projects include image denoising, binarization, shape-reconstruction from photometric stereo with constraints, segmentation of images, quantization, and two-dimensional phase unwrapping.
(Includes joint work with R. Kimmel, A. Kenigsberg, Y. Koren, A. Spira, and G. Dardyk).