Kernel adaptive filter

About: Kernel adaptive filter is a research topic. Over the lifetime, 8771 publications have been published within this topic receiving 142711 citations.

An adaptive dynamically weighted median filter for impulse noise removal

Abstract: A new impulsive noise removal filter, adaptive dynamically weighted median filter (ADWMF), is proposed. A popular method for removing impulsive noise is a median filter whereas the weighted median filter and center weighted median filter were also investigated. ADWMF is based on weighted median filter. In ADWMF, instead of fixed weights, weightages of the filter are dynamically assigned with the results of noise detection. A simple and efficient noise detection method is also used to detect noise candidates and dynamically assign zero or small weights to the noise candidates in the window. This paper proposes an adaptive method which increases the window size according to the amounts of impulsive noise. Simulation results show that the AMWMF works better for both images with low and high density of impulsive noise than existing methods work.

Bi-Exponential Edge-Preserving Smoother

Abstract: Edge-preserving smoothers need not be taxed by a severe computational cost. We present, in this paper, a lean algorithm that is inspired by the bi-exponential filter and preserves its structure—a pair of one-tap recursions. By a careful but simple local adaptation of the filter weights to the data, we are able to design an edge-preserving smoother that has a very low memory and computational footprint while requiring a trivial coding effort. We demonstrate that our filter (a bi-exponential edge-preserving smoother, or BEEPS) has formal links with the traditional bilateral filter. On a practical side, we observe that the BEEPS also produces images that are similar to those that would result from the bilateral filter, but at a much-reduced computational cost. The cost per pixel is constant and depends neither on the data nor on the filter parameters, not even on the degree of smoothing.

Adaptive filter to detect rounded convex regions: iris filter

Abstract: This paper proposes a unique filter, called "iris filter", which evaluates the degree of convergence of gradient vectors in the neighborhood of the pixel of interest. The generalized iris filter and its simplified one are given. The degree of convergence is related to the distribution of orientations of gradient vectors. The region of support of the iris filter is controlled so that the degree of convergence of gradient vectors in it becomes maximum. It means that the size and the shape of the region of support changes adaptively according to the distribution pattern of gradient vectors around the pixel of interest. Theoretical analysis using models of a rounded convex region and a semi-cylindrical region is given. It shows that rounded convex regions are mostly enhanced even if their original contrasts to their background are weak and elongated objects are suppressed. However, the filter output is 1//spl pi/ at the boundaries of rounded convex regions and semi-cylindrical ones in spite of their contrast. This absolute value can be used to detect boundaries of those objects. The proposed filter is effective to enhance and detect rounded convex regions with various sizes and contrasts.

Fault location based on travelling wave identification using an adaptive extended Kalman filter

Abstract: The fault location in transmission systems remains a challenging problem, primarily due to the fault location near the substation ends or the weak fault signals. In this study, an adaptive extended Kalman filter (EKF) based on the maximum likelihood (ML) is proposed to estimate the instantaneous amplitudes of the travelling waves. In this method, the EKF algorithm is used to estimate the optimal states (the clean travelling waves) with additive white noise while ML is used to adaptively optimise the error covariance matrices and the initial conditions of the EKF algorithm. Using the proposed method, the singularity points of travelling waves can be detected, and the exact arrival time of the initial wave head at the substations M and N can be easily yielded. Thus the fault distance can be calculated precisely. The effectiveness of exacting mutation feature using the proposed method has been demonstrated by a simulated instantaneous pulse. Also, the proposed method has been tested with different types of faults, such as different fault locations, different fault resistances and different fault inception angles using ATP simulation. The accuracy of fault location using the proposed method has been compared with conventional wavelet transformation scheme.

Using Random Quasi-Monte-Carlo Within Particle Filters, With Application to Financial Time Series

Abstract: This article presents a new particle filter algorithm which uses random quasi-Monte-Carlo to propagate particles. The filter can be used generally, but here it is shown that for one-dimensional state-space models, if the number of particles is N, then the rate of convergence of this algorithm is N−1. This compares favorably with the N−1/2 convergence rate of standard particle filters. The computational complexity of the new filter is quadratic in the number of particles, as opposed to the linear computational complexity of standard methods. I demonstrate the new filter on two important financial time series models, an ARCH model and a stochastic volatility model. Simulation studies show that for fixed CPU time, the new filter can be orders of magnitude more accurate than existing particle filters. The new filter is particularly efficient at estimating smooth functions of the states, where empirical rates of convergence are N−3/2; and for performing smoothing, where both the new and existing filters have t...