Kinerja OpenMP pada Pengolahan Citra dengan Model Curvature Motion

<p>Evolusi dari sebuah bentuk geometri meliputi perubahan curvature yang terdapat dalam<br />bentuk tersebut. Perubahan curvature ini tidak lepas dari perpindahan titik-titik pembentuknya<br />dan diformulakan sebagai Mean Curvature Motion (MCM). MCM telah dipelajari secara<br />mendalam untuk menyelesaikan salah satunya kurva Jordan pada pemodelan fisis. Pada jurnal<br />ini, solusi MCM diaproksimasi menggunakan skema finite difference dan disimulasikan ke<br />dalam paralel OpenMP. Untuk menghitung performansi paralel, dilakukan simulasi sebanyak<br />10 niter berbeda pada thread sejumlah 2, 4, dan 8. Dari simulasi yang telah dilakukan,<br />didapatkan hasil bahwa performa paralel lebih membutuhkan waktu komputasi yang lebih<br />rendah daripada serial. Selain itu, didapat pula rata-rata efisiensi kode paralel menggunakan<br />2 Thread lebih tinggi daripada menggunakan 4 Thread dan 8 Thread. Sebagai contoh pada<br />ukuran niter 50000, kecepatan masing-masing 2, 4, dan 8 Thread adalah 180.422, 156.002,<br />dan 333.243 s, serta efisiensi masing-masing 2, 4, dan 8 Thread adalah 113%, 66%, dan<br />34,8%.</p>

Image Restoration Using Adaptive Region-Wise p-Norm Filter with Local Constraints

In this work, we introduce a feature adaptive second-order p-norm filter with local constraints for image restoration and texture preservation. The p-norm value of the filter is chosen adaptively between 1 and 2 in a local region based on the regional image characteristics. The filter behaves like a mean curvature motion (MCM) [A. Marquina and S. Osher, SIAM Journal of Scientific Computing 22, 387–405 (2000)] in the regions where the p-norm value is 1 and switches to a Laplacian filter in the rest of the regions (where the p-norm value is 2). The proposed study considerably reduces stair-case effect and effectively removes noise from images while deblurring them. The noise is assumed as Gaussian distributed (with zero mean and variance [Formula: see text]) and blur is linearly shift invariant (out-of-focus). The filter converges at a faster rate with semi-implicit Crank–Nicholson scheme. The regularization parameter is initialized and updated based on the local image features and therefore this filter preserves edges, structures, textures and fine details present in images very well. The method is applied on different kinds of images with different image characteristics. We show the response of the filter to various kinds of images and numerically quantify the performance in terms of standard statistical measures.

The Influence of the Exterior Surface on Grain Boundary Mobility Measurements

Polycrystalline materials typically contain a very large number of grains whose surrounding grain boundaries evolve over time to reducethe overall energy of the microstructure. The evolution of the microstructure is influencedby the motion of the exterior surface since the grain boundaries couple to the exterior surface of the specimen; these effects can be appreciable especially in thin specimens. We model these effects using the classical framework of Mullins, in whichgrain boundaries move by mean curvature motion, Vn =A κ, and the exterior surface evolves by surface diffusion, Vn = -BΔs κ. Here Vn and κ denote the normal velocity and the mean curvature of the respective evolving surfaces, and Δs is the surface Laplacian. A classical way to determine A, the ``reduced mobility," is to make measurements based on the half-loop bicrystalline geometry. In this geometry one of the two grains, which embedded within the other, recedes at a roughly constant rate which can provide an estimate for A. In this note, we report on findings concerning the effects of the exterior surface on grain boundary motion and mobility measurements in the context of the half-loop bicrystalline geometry. We assume that the ratio of grain boundary energy to the exterior surface energy is small, and suitable assumptions are made of the specimen aspect ratio.