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.

Optimization of short digital linear filters for increased accuracy

Abstract: The accuracy of short length digital linear filter operators can be substantially increased if the sampling interval as well as the abscissa shift are properly adjusted. This may be done by a trial and error process of adjustment of these parameters until the error made by the filter operator, applied to a suitably chosen test function, is smallest. As an illustration of the application of this method, 7-, 11- and 19-point filters for the calculation of Schlumberger apparent resistivity from a known resistivity transform are designed. Errors with the new 7-point filter are seen to be less than those with a 19-point filter of conventional design. The errors with the new 19-point filter are two to three orders of magnitude smaller than those made by the conventional 19-point filter. The new method should provide digital linear operators that allow significant improvements in accuracy for comparable computation efforts, or substantial reduction in computation for comparable accuracy of results, or something of both.

Efficient Design of Cosine-Modulated Filter Banks via Convex Optimization

Abstract: This paper presents efficient approaches for designing cosine-modulated filter banks with linear phase prototype filter. First, we show that the design problem of the prototype filter being a spectral factor of 2M th-band filter is a nonconvex optimization problem with low degree of nonconvexity. As a result, the nonconvex optimization problem can be cast into a semi-definite programming (SDP) problem by a convex relaxation technique. Then the reconstruction error is further minimized by an efficient iterative algorithm in which the closed-form expression is given in each iteration. Several examples are given to illustrate the effectiveness of the proposed method over the existing ones.

Signal processing arrangement including variable length adaptive filter and method therefor

Abstract: A signal processor includes an adaptive filter that generates an output signal as a function of an input signal and of weighting coefficients stored in a memory. An error detector generates an error signal as a function of the output signal. The error signal is used in adjusting the weighting coefficients. A data processor adjusts the number of weighting coefficients of the adaptive filter and system resources used for storing the weighting coefficients as a function of a characteristic of the weighting coefficients.

Motion and detail adaptive denoising of video

Abstract: Non-linear techniques for denoising images and video are known to be superior to linear ones. In addition video denoising using spatio-temporal information is considered to be more efficient compared with the use of just temporal information in the presence of fast motion and low noise. Earlier, we introduced a 3-D extension of the K-nearest neighbor filter and have investigated its properties. In this paper we propose a new, motion- and detail-adaptive filter, which solves some of the potential drawbacks of the non-adaptive version: motion caused artifacts and the loss of fine details and texture. We also introduce a novel noise level estimation technique for automatic tuning of the noise-level dependent parameters. The results show that the adaptive K-nearest neighbor filter outperforms the none-adaptive one, as well as some other state-of-the-art spatio-temporal filters such as the 3D alpha-trimmed mean and the state-of-the-art rational filter by Ramponi from both a PSNR and visual quality point of view.