It is well known that spatial averaging can be realized (in space or frequency domain) using algorithms whose complexity does not scale with the size or shape of the filter. These fast algorithms are generally referred to as constant-time or O(1) algorithms in the image-processing literature. Along with the spatial filter, the edge-preserving bilateral filter involves an additional range kernel...

This paper presents a Comparative Study of NLMS (Normalized Least Mean Square) and ENSS (Error Normalized Step Size) LMS (Least Mean Square) algorithms. For this System Identification (An Adaptive Filter Application) is considered. Three performances Criterion are utilized in this study: Minimum Mean Square error (MSE), Convergence Speed, the Algorithm Execution Time. The Step Size Parameter (μ...

Lagrangian averaging (LA) is producing new ideas and methods for the construction of novel subgrid models for momentum, energy and scalar transport in turbulence. LA is applied at fixed Lagrangian fluid label, rather than at fixed spatial location, as in Eulerian averaging and filtering for RANS and LES equations, respectively. Instead, LA is performed in a frame of motion that follows along wi...

Magnetic resonance imaging (MRI) still suffers from a low signal-to-noise ratio (SNR), especially for real-time imaging sequences. In order to increase the SNR of a sequence of images, an adapted weighted averaging (AWA) approach is proposed and applied to coronary angiography imaging. It can in principle be used for any sequence of 2D images, e.g. fMRI or 3D data sets as well as in CT for fluo...

Averaging a set of time series is a major topic for many temporal data mining tasks as summarization, extracting prototype or clustering. Time series averaging should deal with the tricky multiple temporal alignment problem; a still challenging issue in various domains. This work compares the major progressive and iterative averaging time series methods under dynamic time warping (dtw).

Weighted averaging filters and nonlinear partial differential equations (PDEs) are two popular concepts for discontinuity-preserving denoising. In this paper we investigate novel relations between these filter classes: We deduce new PDEs as the scaling limit of the spatial step size of discrete weighted averaging methods. In the one-dimensional setting, a simple weighted averaging of both neigh...

Morphological image processing filters preserve shapes related to the structuring element shape of the operator. The basic morphological operators are minimum (erosion) and maximum (dilation) operations performed on the pixels within a structuring element. Although these operators (and the compound operators formed from them) are able to smooth noise, they also introduce a statistical and deter...

The restoration of an entire epicortical ECoG-potential pattern from spatially sampled EEG-potentials can be performed according to the general principle of inverse spatial filtering: if a forward spatial filter is an averaging one, then a mutually inverse spatial filter can form a sharpened pattern from a slurred sampled pattern thus restoring more or less a spatial contrast of a primary sourc...