Blurring the image while preserving the important features such as edges is a crucial study in computer vision. This paper presents the results of applying three iterative methods which are Jacobi, Gauss Seidel and Successive Overrelaxation (SOR) to solve anisotropic diffusion equation for image blurring, where the output image of Jacobi is used as a control image. The number of iterations and computational time required to solve the anisotropic diffusion equation are used to measure the performance of the considered iterative methods. The findings show that SOR method is more efficient to smooth the inner region of an image compared to Jacobi and Gauss-Seidel methods in which the SOR required the least number of iterations and computational time.
The non-linear diffusion equation is known to be a significant application in solving image processing issues. The equations provided the image filtering techniques that blurring the image without degrade the edge information which is also one of crucial study in computer vision. Nonetheless, an intense amount of computations is needed in filtering the image as the sizes that keep getting bigger. Along these lines, this paper constructs an analysis to speed up the required computation in solving the developed linear system with the faster iterative method, i.e. two-point Explicit Group Successive Over-Relaxation or known as 2-EGSOR. For the performances comparison, the standard Gauss-Seidel (GS), Successive Over-Relaxation (SOR) and 2-EGSOR iterative method will be set to produce almost the similar quality image of Jacobi iterative method measured by using percentage error of the overall pixels difference. Subsequently, it is discovered the 2-EGSOR offers faster approach to blur the image compared the others iterative methods with the least iterations and computational time.
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