Topic

# Filter design

About: Filter design is a(n) research topic. Over the lifetime, 26483 publication(s) have been published within this topic receiving 400503 citation(s).

##### Papers published on a yearly basis

##### Papers

More filters

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01 Apr 1993TL;DR: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters, represented as a set of random samples, which are updated and propagated by the algorithm.

Abstract: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linear- ity or Gaussian noise: it may be applied to any state transition or measurement model. A simula- tion example of the bearings only tracking problem is presented. This simulation includes schemes for improving the efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter.

7,559 citations

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01 Jul 1992

Abstract: 1. Introduction 2. Review of Discrete-Time Systems 3. Review of Digital Filters 4. Fundamentals of Multirate Systems 5. Maximally Decimated Filter Banks 6. Paraunitary Perfect Reconstruction Filter Banks 7. Linear Phase Perfect Reconstruction QMF Banks 8. Cosine Modulated Filter Banks 9. Finite Word Length Effects 10. Multirate Filter Bank Theory and Related Topics 11. The Wavelet Transform and Relation to Multirate Filter Banks 12. Multidimensional Multirate Systems 13. Review of Discrete-Time Multi-Input Multi-Output LTI Systems 14. Paraunitary and Lossless Systems Appendices Bibliography Index

4,756 citations

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TL;DR: The guided filter is a novel explicit image filter derived from a local linear model that can be used as an edge-preserving smoothing operator like the popular bilateral filter, but it has better behaviors near edges.

Abstract: In this paper, we propose a novel explicit image filter called guided filter. Derived from a local linear model, the guided filter computes the filtering output by considering the content of a guidance image, which can be the input image itself or another different image. The guided filter can be used as an edge-preserving smoothing operator like the popular bilateral filter [1], but it has better behaviors near edges. The guided filter is also a more generic concept beyond smoothing: It can transfer the structures of the guidance image to the filtering output, enabling new filtering applications like dehazing and guided feathering. Moreover, the guided filter naturally has a fast and nonapproximate linear time algorithm, regardless of the kernel size and the intensity range. Currently, it is one of the fastest edge-preserving filters. Experiments show that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications, including edge-aware smoothing, detail enhancement, HDR compression, image matting/feathering, dehazing, joint upsampling, etc.

3,721 citations

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TL;DR: Modifications to the fitting procedure are described which allow more accurate derivations of filter shapes derived from data where the notch is always placed symmetrically about the signal frequency and when the underlying filter is markedly asymmetric.

Abstract: A well established method for estimating the shape of the auditory filter is based on the measurement of the threshold of a sinusoidal signal in a notched-noise masker, as a function of notch width. To measure the asymmetry of the filter, the notch has to be placed both symmetrically and asymmetrically about the signal frequency. In previous work several simplifying assumptions and approximations were made in deriving auditory filter shapes from the data. In this paper we describe modifications to the fitting procedure which allow more accurate derivations. These include: 1) taking into account changes in filter bandwidth with centre frequency when allowing for the effects of off-frequency listening; 2) correcting for the non-flat frequency response of the earphone; 3) correcting for the transmission characteristics of the outer and middle ear; 4) limiting the amount by which the centre frequency of the filter can shift in order to maximise the signal-to-masker ratio. In many cases, these modifications result in only small changes to the derived filter shape. However, at very high and very low centre frequencies and for hearing-impaired subjects the differences can be substantial. It is also shown that filter shapes derived from data where the notch is always placed symmetrically about the signal frequency can be seriously in error when the underlying filter is markedly asymmetric. New formulae are suggested describing the variation of the auditory filter with frequency and level. The implications of the results for the calculation of excitation patterns are discussed and a modified procedure is proposed. The appendix lists FORTRAN computer programs for deriving auditory filter shapes from notched-noise data and for calculating excitation patterns. The first program can readily be modified so as to derive auditory filter shapes from data obtained with other types of maskers, such as rippled noise.

2,317 citations