Inviscid regularization modeling of turbulent flow is investigated. Homogeneous, isotropic, decaying turbulence is simulated at a range of filter widths. A coarse-graining of turbulent flow arises from the direct regularization of the convective nonlinearity in the Navier–Stokes equations. The regularization is translated into its corresponding sub-filter model to close the equations for large-...

The wavelet frame systems have been playing an active role in image restoration and many other image processing fields over the past decades, owing to the good capability of sparsely approximating piece-wise smooth functions such as images. In this paper, we propose a novel wavelet frame based sparse recovery model called Support Driven Sparse Regularization (SDSR) for image deblurring, where t...

In this paper, we investigate the use of the non-local means (NLM) denoising approach in the context of image deblurring and restoration. We propose a novel deblurring approach that utilizes a non-local regularization constraint. Our interest in the NLM principle is its potential to suppress noise while effectively preserving edges and texture detail. Our approach leads to an iterative cost fun...

High-frequency and correlated noise filtering is one of the important preprocessing steps for GRACE level-2 products before calculating mass anomaly. Decorrelation denoising kernel (DDK) filters are usually considered as such optimal to solve this problem. In work, a sparse DDK filter proposed. This achieved by replacing Tikhonov regularization in traditional with weighted L1 norm regularizatio...

In this paper, a technique to determine the filter bank coefficients of Wavelets db3 and coif2 using mean square error adaptive filter RLS algorithm has been presented. Filter bank coefficients of the wavelet are treated as weight vector of adaptive filter and are changed iteratively to reach the desired value after few iteration. It is found that of the three adaptive algorithms viz. Least Mea...

We present a fundamentally new autonomic subgridscale closure for large eddy simulations (LES) that solves a nonlinear, nonparametric system identification problem instead of using a predefined turbulence model. The autonomic approach expresses the local SGS stress tensor as the most general unknown nonlinear function of the resolvedscale primitive variables at all locations and times using a V...