Cataloged from PDF version of article.In this thesis, we are concerned with the image restoration problem which has
been formulated in the literature as a system of linear inequalities. With this formulation,
the resulting constraint matrix is an unstructured sparse-matrix and
even with small size images we end up with huge matrices. So, to solve the
restoration problem, we have used the surrogate constraint methods, that can
work efficiently for large size problems and are amenable for parallel implementations.
Among the surrogate constraint methods, the basic method considers all
of the violated constraints in the system and performs a single block projection
in each step. On the other hand, parallel method considers a subset of the constraints,
and makes simultaneous block projections. Using several partitioning
strategies and adopting different communication models we have realized several
parallel implementations of the two methods. We have used the hypergraph partitioning
based decomposition methods in order to minimize the communication
costs while ensuring load balance among the processors. The implementations
are evaluated based on the per iteration performance and on the overall performance.
Besides, the effects of different partitioning strategies on the speed of
convergence are investigated. The experimental results reveal that the proposed
parallelization schemes have practical usage in the restoration problem and in
many other real-world applications which can be modeled as a system of linear
inequalities.Malas, TahirM.S
