Browsing by Subject "Image restoration"

Now showing 1 - 4 of 4

Open Access
Novel methods in image halftoning
(1998) Bozkurt, Gözde
Halftoning refers to the problem of rendering continuous-tone (contone) images on display and printing devices which are capable of reproducing only a limited number of colors. A new adaptive halftoning method using the adaptive QR- RLS algorithm is developed for error diffusion which is one of the halftoning techniques. Also, a diagonal scanning strategy to exploit the human visual system properties in processing the image is proposed. Simulation results on color images demonstrate the superior quality of the new method compared to the existing methods. Another problem studied in this thesis is inverse halftoning which is the problem of recovering a contone image from a given halftoned image. A novel inverse halftoning method is developed for restoring a contone image from the halftoned image. A set theoretic formulation is used where sets are defined using the prior information about the problem. A new space domain projection is introduced assuming the halftoning is performed ,with error diffusion, and the error diffusion filter kernel is known. The space domain, frequency domain, and space-scale domain projections are used alternately to obtain a feasible solution for the inverse halftoning problem which does not have a unique solution. Simulation results for both grayscale and color images give good results, and demonstrate the effectiveness of the proposed inverse halftoning method.
Open Access
Parallel algorithms for the solution of large sparse inequality systems on distributed memory architectures
(1998) Turna, Esma
In this thesis, several parallel algorithms are proposed and utilized for the solution of large sparse linear inequality systems. The parallelization schemes are developed from the coarse-grain parallel formulation of the surrogate constraint method, based on the partitioning strategy: 1D partitioning and 2D partitioning. Furthermore, a third parallelization scheme is developed for the explicit minimization of the communication overhead in 1D partitioning, by using hypergraph partitioning. Utilizing the hypergraph model, the communication overhead is maintained via a global communication scheme and a local communication scheme. In addition, new algorithms that use the bin packing heuristic are investigated for efficient load balancing in uniform rowwise stripped and checkerboard partitioning. A general class of image recovery problems is formulated as a linear inequality system. The restoration of images blurred by so called point spread functions arising from effects such as misfocus of the photographic device, atmospheric turbulence, etc. is successfully provided with the developed parallel algorithms.
Open Access
Parallel image restoration using surrogate constraint methods
(Academic Press, 2007) Uçar, B.; Aykanat, Cevdet; Pınar, M. Ç.; Malas, T.
When formulated as a system of linear inequalities, the image restoration problem yields huge, unstructured, sparse matrices even for images of small size. To solve the image restoration problem, we use the surrogate constraint methods that can work efficiently for large problems. Among variants of the surrogate constraint method, we consider a basic method performing a single block projection in each step and a coarse-grain parallel version making simultaneous block projections. Using several state-of-the-art partitioning strategies and adopting different communication models, we develop competing parallel implementations of the two methods. The implementations are evaluated based on the per iteration performance and on the overall performance. The experimental results on a PC cluster reveal that the proposed parallelization schemes are quite beneficial.
Open Access
Restoration of space-variant global blurs caused by severe camera movements and coordinate distortions
(IOP Science, 1998) Özaktaş, H.; Pınar, M. Ç.; Akgül, M.
We show that a broad class of image recovery problems where an object undergoing an arbitrary two-dimensional, time- and space-variant, non-separable, nonlinear global coordinate distortion, is imaged for a certain duration, can be formulated as a system of linear inequalities. Since the system of inequalities arising in this context can be solved efficiently, our approach yields an effective method for solving this class of image recovery problems. A novel step size policy is introduced to accelerate the parallel surrogate constraint algorithm employed. The approach is illustrated by recovering an image severely blurred by the combined effects of translational and rotational motion and elliptic scaling.