Recently, another approach to study incompressible fluid flow was suggested [S. Ansumali, I. Karlin, and H. Ottinger, Phys. Rev. Lett. 94, 080602 (2005)]-the kinetically reduced local Navier-Stokes (KRLNS) equations. We consider a simplified two-dimensional KRLNS system and compare it with Chorin's artificial compressibility method. A comparison of the two methods for steady state computation of the flow in a lid-driven cavity at various Reynolds numbers shows that the results from both methods are in good agreement with each other. However, in the transient flow, it is demonstrated that the KRLNS equations correctly describe the time evolution of the velocity and of the pressure, unlike the artificial compressibility method.

In this paper, we propose an orientation-matching functional minimization for image denoising and image inpainting. Following the two-step TV-Stokes algorithm (Rahman et al. in Scale space and variational methods in computer vision, pp. 473-482, Springer, Heidelberg, 2007;Tai et al. in Image processing based on partial differential equations, pp. 3-22, Springer, Heidelberg, 2006; Bertalmio et al. in Proc. conf. comp. vision pattern rec., pp. 355-362, 2001), a regularized tangential vector field with zero divergence condition is first obtained. Then a novel approach to reconstruct the image is proposed. Instead of finding an image that fits the regularized normal direction from the first step, we propose to minimize an orientation matching cost measuring the alignment between the image gradient and the regularized normal direction. This functional yields a new nonlinear partial differential equation (PDE) for reconstructing denoised and inpainted images. The equation has an adaptive diffusivity depending on the orientation of the regularized normal vector field, providing reconstructed images which have sharp edges and smooth regions. The additive operator splitting (AOS) scheme is used for discretizing Euler-Lagrange equations. We present the results of various numerical experiments that illustrate the improvements obtained with the new functional.

Abstract. In this paper, we propose an orientation-matching minimization for denoising digital images with an additive noise. Inspired [1][2][3] by the two-step algorithm in the TV-Stokes denoising process, the regularized tangential vector field with the zero divergence condition is used in the first step. The present work suggests a different approach in order to reconstruct a denoised image in the second step. Namely, instead of finding an image that fits the regularized normal direction from the first step, we minimize an orientation between the image gradient and the regularized normal direction. It gives a nonlinear partial differential equation (PDE) for reconstructing denoised images, which has the diffusivity depending on an orientation of a regularized normal vector field and the weighted self-adaptive force term depending on the direction between the gradient of an image and the vector field. This allows to obtain a denoised image which has sharp edges and smooth regions, even though an original image has smoothly changing pixel values near sharp edges. The additive operator splitting scheme is used for discretizing Euler-Lagrange equations. We show improved qualities of results from various numerical experiments.