Showing papers on "Kernel adaptive filter published in 2013"

Guided Image Filtering

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.

Optimal Linear Filters for Discrete-Time Systems With Randomly Delayed and Lost Measurements With/Without Time Stamps

Abstract: A novel model is developed to describe possible random delays and losses of measurements transmitted from a sensor to a filter by a group of Bernoulli distributed random variables. Based on the new developed model, an optimal linear filter dependent on the probabilities is presented in the linear minimum variance sense by the innovation analysis approach when packets are not time-stamped. The solution to the optimal linear filter is given in terms of a Riccati difference equation and a Lyapunov difference equation. A sufficient condition for the existence of the steady-state filter is given. At last, the optimal filter is given by Kalman filter when packets are time-stamped.

Image denoising based on non-local means filter and its method noise thresholding

Abstract: Non-local means filter uses all the possible self-predictions and self-similarities the image can provide to determine the pixel weights for filtering the noisy image, with the assumption that the image contains an extensive amount of self-similarity. As the pixels are highly correlated and the noise is typically independently and identically distributed, averaging of these pixels results in noise suppression thereby yielding a pixel that is similar to its original value. The non-local means filter removes the noise and cleans the edges without losing too many fine structure and details. But as the noise increases, the performance of non-local means filter deteriorates and the denoised image suffers from blurring and loss of image details. This is because the similar local patches used to find the pixel weights contains noisy pixels. In this paper, the blend of non-local means filter and its method noise thresholding using wavelets is proposed for better image denoising. The performance of the proposed method is compared with wavelet thresholding, bilateral filter, non-local means filter and multi-resolution bilateral filter. It is found that performance of proposed method is superior to wavelet thresholding, bilateral filter and non-local means filter and superior/akin to multi-resolution bilateral filter in terms of method noise, visual quality, PSNR and Image Quality Index.

Square Root Cubature Information Filter

Abstract: Nonlinear state estimation plays a major role in many real-life applications. Recently, some sigma-point filters, such as the unscented Kalman filter, the particle filter, or the cubature Kalman filter have been proposed as promising substitutes for the conventional extended Kalman filter. For multisensor fusion, the information form of the Kalman filter is preferred over standard covariance filters due to its simpler measurement update stage. This paper presents a new state estimation algorithm called the square root cubature information filter (SRCIF) for nonlinear systems. The cubature information filter is first derived from an extended information filter and a recently developed cubature Kalman filter. For numerical accuracy, its square root version is then developed. Unlike the extended Kalman or extended information filters, the proposed filter does not require the evaluation of Jacobians during state estimation. The proposed approach is further extended for use in multisensor state estimation. The efficacy of the SRCIF is demonstrated by a simulation example of a permanent magnet synchronous motor.

Prototype Filter Optimization to Minimize Stopband Energy With NPR Constraint for Filter Bank Multicarrier Modulation Systems

Abstract: Recently, filter bank multicarrier (FBMC) modulations have attracted increasing attention. The filter banks of FBMC are derived from a prototype filter that determines the system performance, such as stopband attenuation, intersymbol interference (ISI) and interchannel interference (ICI). In this paper, we formulate a problem of direct optimization of the filter impulse-response coefficients for the FBMC systems to minimize the stopband energy and constrain the ISI/ICI. Unfortunately, this filter optimization problem is nonconvex and highly nonlinear. Nevertheless, observing that all the functions in the optimization problem are twice-differentiable, we propose using the $\alpha$ -based Branch and Bound ( $\alpha$ BB) algorithm to obtain the optimal solution. However, the convergence time of the algorithm is unacceptable because the number of unknowns (i.e., the filter coefficients) in the optimization problem is too large. The main contribution of this paper is that we propose a method to dramatically reduce the number of unknowns of the optimization problem through approximation of the constraints, so that the optimal solution of the approximated optimization problem can be obtained with acceptable computational complexity. Numerical results show that, the proposed approximation is reasonable, and the optimized filters obtained with the proposed method achieve significantly lower stopband energy than those with the frequency sampling and windowing based techniques.

Image denoising based on gaussian/bilateral filter and its method noise thresholding

Abstract: The Gaussian filter is a local and linear filter that smoothes the whole image irrespective of its edges or details, whereas the bilateral filter is also a local but non-linear, considers both gray level similarities and geometric closeness of the neighboring pixels without smoothing edges The extension of bilateral filter: multi-resolution bilateral filter, where bilateral filter is applied to approximation subbands of an image decomposed and after each level of wavelet reconstruction The application of bilateral filter on the approximation subband results in loss of some image details, whereas that after each level of wavelet reconstruction flattens the gray levels thereby resulting in a cartoon-like appearance To tackle these issues, it is proposed to use the blend of Gaussian/bilateral filter and its method noise thresholding using wavelets In Gaussian noise scenarios, the performance of proposed methods is compared with existing denoising methods and found that, it has inferior performance compared to Bayesian least squares estimate using Gaussian Scale mixture and superior/comparable performance to that of wavelet thresholding, bilateral filter, multi-resolution bilateral filter, NL-means and Kernel based methods Further, proposed methods have the advantage of less computational time compared to other methods except wavelet thresholding, bilateral filter

Study of the General Kalman Filter for Echo Cancellation

Abstract: The Kalman filter is a very interesting signal processing tool, which is widely used in many practical applications. In this paper, we study the Kalman filter in the context of echo cancellation. The contribution of this work is threefold. First, we derive a different form of the Kalman filter by considering, at each iteration, a block of time samples instead of one time sample as it is the case in the conventional approach. Second, we show how this general Kalman filter (GKF) is connected with some of the most popular adaptive filters for echo cancellation, i.e., the normalized least-mean-square (NLMS) algorithm, the affine projection algorithm (APA) and its proportionate version (PAPA). Third, a simplified Kalman filter is developed in order to reduce the computational load of the GKF; this algorithm behaves like a variable step-size adaptive filter. Simulation results indicate the good performance of the proposed algorithms, which can be attractive choices for echo cancellation.

Analytical Approach to Wave Field Reconstruction Filtering in Spatio-Temporal Frequency Domain

Abstract: For transmission of a physical sound field in a large area, it is necessary to transform received signals of a microphone array into driving signals of a loudspeaker array to reproduce the sound field. We propose a method for transforming these signals by using planar or linear arrays of microphones and loudspeakers. A continuous transform equation is analytically derived based on the physical equation of wave propagation in the spatio-temporal frequency domain. By introducing spatial sampling, the uniquely determined transform filter, called a wave field reconstruction filter (WFR filter), is derived. Numerical simulations show that the WFR filter can achieve the same performance as that obtained using the conventional least squares (LS) method. However, since the proposed WFR filter is represented as a spatial convolution, it has many advantages in filter design, filter size, computational cost, and filter stability over the transform filter designed by the LS method.

Variable Step-Size Sign Subband Adaptive Filter

Abstract: This letter proposes a variable step-size sign subband adaptive filter (SSAF) based on the minimization of mean-square deviation (MSD). In the process of minimizing the MSD, because it is not feasible to know the exact value of the MSD, the step size is derived by minimizing the upper bound of the MSD in each iteration. The proposed algorithm uses this step size in the SSAF update equation so as to improve the filter performance in terms of the convergence rate and the steady-state estimation error. The proposed algorithm is tested in a system-identification scenario that includes impulsive noise. Simulation results show that the proposed algorithm performs better than the previous algorithms.

Robust topology optimization accounting for misplacement of material

Abstract: The use of topology optimization for structural design often leads to slender structures. Slender structures are sensitive to geometric imperfections such as the misplacement or misalignment of material. The present paper therefore proposes a robust approach to topology optimization taking into account this type of geometric imperfections. A density filter based approach is followed, and translations of material are obtained by adding a small perturbation to the center of the filter kernel. The spatial variation of the geometric imperfections is modeled by means of a vector valued random field. The random field is conditioned in order to incorporate supports in the design where no misplacement of material occurs. In the robust optimization problem, the objective function is defined as a weighted sum of the mean value and the standard deviation of the performance of the structure under uncertainty. A sampling method is used to estimate these statistics during the optimization process. The proposed method is successfully applied to three example problems: the minimum compliance design of a slender column-like structure and a cantilever beam and a compliant mechanism design. An extensive Monte Carlo simulation is used to show that the obtained topologies are more robust with respect to geometric imperfections.

Downlink tone detection and adaptation of a secondary path response model in an adaptive noise canceling system

Abstract: An adaptive noise canceling (ANC) circuit adaptively generates an anti-noise signal from a reference microphone signal that is injected into the speaker or other transducer output to cause cancellation of ambient audio sounds. An error microphone proximate the speaker provides an error signal. A secondary path estimating adaptive filter estimates the electro-acoustical path from the noise canceling circuit through the transducer so that source audio can be removed from the error signal. Tones in the source audio, such as remote ringtones, present in downlink audio during initiation of a telephone call, are detected by a tone detector using accumulated tone persistence and non-silence hangover counting, and adaptation of the secondary path estimating adaptive filter is halted to prevent adapting to the tones. Adaptation of the adaptive filters is then sequenced so any disruption of the secondary path adaptive filter response is removed before allowing the anti-noise generating filter to adapt.

Low-Area and High-Throughput Architecture for an Adaptive Filter Using Distributed Arithmetic

Abstract: A high-performance implementation scheme for a least mean square adaptive filter is presented. The architecture is based on distributed arithmetic in which the partial products of filter coefficients are precomputed and stored in lookup tables (LUTs) and the filtering is done by shift-and-accumulate operations on these partial products. In the case of an adaptive filter, it is required that the filter coefficients be updated and, hence, these LUTs are to be recalculated. A new strategy based on the offset binary coding scheme has been proposed in order to update these LUTs from time to time. Simulation results show that the proposed scheme consumes very less chip area and operates at high throughput for large base unit size k ( = N/m) , where m is an integer and N is the number of filter coefficients. For example, a 128-tap finite-impulse-response adaptive filter with the proposed implementation produces 12 times more throughput (for k = 8) and consumes almost 26% less area when compared to the best of existing architectures.

LLSURE: Local Linear SURE-Based Edge-Preserving Image Filtering

Abstract: In this paper, we propose a novel approach for performing high-quality edge-preserving image filtering. Based on a local linear model and using the principle of Stein's unbiased risk estimate as an estimator for the mean squared error from the noisy image only, we derive a simple explicit image filter which can filter out noise while preserving edges and fine-scale details. Moreover, this filter has a fast and exact linear-time algorithm whose computational complexity is independent of the filtering kernel size; thus, it can be applied to real time image processing tasks. The experimental results demonstrate the effectiveness of the new filter for various computer vision applications, including noise reduction, detail smoothing and enhancement, high dynamic range compression, and flash/no-flash denoising.

Ambient noise-based adaptation of secondary path adaptive response in noise-canceling personal audio devices

Abstract: An adaptive noise canceller adapts a secondary path modeling response using ambient noise, rather than using another noise source or source audio as a training source. Anti-noise generated from a reference microphone signal using a first adaptive filter is used as the training signal for training the secondary path response. Ambient noise at the error microphone is removed from an error microphone signal, so that only anti-noise remains. A primary path modeling adaptive filter is used to modify the reference microphone signal to generate a source of ambient noise that is correlated with the ambient noise present at the error microphone, which is then subtracted from the error microphone signal to generate the error signal. The primary path modeling adaptive filter is previously adapted by minimizing components of the error microphone signal appearing in an output of the primary path adaptive filter while the anti-noise signal is muted.

Adaptive robust Kalman filter for relative navigation using global position system

Abstract: An adaptive robust Kalman filter algorithm is derived to account for both process noise and measurement noise uncertainty. The adaptive algorithm estimates process noise covariance based on the recursive minimisation of the difference between residual covariance matrix given by the filter and that calculated from time-averaging of the residual sequence generated by the filter at each time step. A recursive algorithm is proposed based on both Massachusetts Institute of Technology (MIT) rule and typical non-linear extended Kalman filter equations for minimising the difference. The measurement update using a robust technique to minimise a criterion function originated from Huber filter. The proposed adaptive robust Kalman filter has been successfully implemented in relative navigation using global position system for spacecraft formation flying in low earth orbit, with real-orbit perturbations and non-Gaussian random measurement errors. The numerical simulation results indicate that the proposed adaptive robust filter can provide better relative navigation performance in terms of accuracy and robustness as compared with previous filter algorithms.

Edge detection of potential field data using improved local phase filter

Abstract: Edge detection is a requisite task in the interpretation of potential field data. There are many high-pass filters based on horizontal and vertical derivatives in use, such as total horizontal derivative, tilt angle, theta map, et al. In this paper, we present a new edge detection filter, which uses the combination of the different order horizontal derivatives to delineate the edges of the sources, called improved local phase (ILP) filter. The new filter is computationally stable, as it does not need the computation of the vertical derivatives of potential field data. The new filter is tested on synthetic and real potential field data. The resolving power of the ILP filter is tested by comparing the results with those obtained by the other filters. The advantage of the ILP filter in the edge detection of potential field data is due to the fact that it can display the edges of the causative sources more precisely and clearly, and can bring out more subtle details.

Multi-rate filter system

Abstract: A multi-rate filter system is disclosed. More particularly, a computationally efficient multi-rate filter system for processing an audio stream on a consumer electronics device is disclosed. The multi-rate filter system includes a plurality of multi-rate filtering blocks, at least one block including a linear filter component. At least one multi-rate filtering block may include a nonlinear signal processing components. The multi-rate filter system may include a nonlinear functional block. A method of filtering a signal is also disclosed.

Systems and methods for adaptive noise cancellation including dynamic bias of coefficients of an adaptive noise cancellation system

Abstract: In accordance with method and systems of the present disclosure, a processing circuit may implement an adaptive filter having a response that generates the anti-noise signal from the reference microphone signal to reduce the presence of the ambient audio sounds heard by the listener, a coefficient control block that shapes the response of the adaptive filter in conformity with the error microphone signal and the reference microphone signal by adapting the response of the adaptive filter to minimize the ambient audio sounds in the error microphone signal, and a coefficient bias control block which biases coefficients of the coefficient control block towards zero in a range of frequencies outside of a frequency response of the source audio signal.

Analog digital converter (adc) and correction circuit and correction method therefor

Abstract: An ADC has ADC channels converting an analog input signal into an digital output signal in a time interleave manner; a channel combiner combining channel digital signals respectively output by the ADC channels and generate the digital output signal; an adaptive filter provided at one of the plurality of ADC channels; and a correction circuit detecting a skew error in the digital output signal, generating a coefficient of the adaptive filter according to the skew error for setting it in the filter. According to the skew error, in a first setting, the correction circuit sets the coefficient such that the adaptive filter phase-shifts to one direction a phase of the channel digital signal and, in a second setting, the correction circuit sets the coefficient such that the adaptive filter phase-shifts to an opposite direction and sets a coefficient with which the skew error is suppressed to a desired level.

Estimation precision comparison of Cubature Kalman filter and Unscented Kalman filter

Abstract: In order to select the appropriate filtering method from the UKF and CKF for the different dimensions nonlinear systems estimation,the two filters are analyzed and compared through the Taylor expansion of function and the numerical stability.Due to the different dimension,the captured high-order item degree of function Taylor expansion and the numerical stability are different to appear different filter precisions,so that the filter choice ways of different dimension are acquired.Simulation results show the correctness of with the theoretical analysis.

A comparative study of kernel adaptive filtering algorithms

Abstract: Kernel adaptive filtering is a growing field of signal processing that is concerned with nonlinear adaptive filtering. When implemented naively, the time and memory complexities of these algorithms grow at least linearly with the amount of data processed. A large number of practical solutions have been proposed throughout the last decade, based on sparsification or pruning mechanisms. Nevertheless, there is a lack of understanding of their relative merits, which often depend on the data they operate on. We propose to study the quality of the solution as a function of either the time or the memory complexity. We empirically test six different kernel adaptive filtering algorithms on three different benchmark data sets. We make our code available through an open source toolbox that includes additional algorithms and allows to measure the complexities explicitly in number of floating point operations and bytes needed, respectively.

Systems and methods for adaptive noise cancellation by biasing anti-noise level

Abstract: A processing circuit may comprise an adaptive filter having a response generating an anti-noise signal from a reference microphone signal, a secondary path estimate filter modeling an electro-acoustic path of a source audio signal, a biasing portion that generates a scaled anti-noise signal by applying a scaling factor and the response of the secondary path estimate filter to the anti-noise signal, and a coefficient control block that shapes the response of the adaptive filter in conformity with the reference microphone signal and a modified playback corrected error signal by adapting the response of the adaptive filter to minimize ambient audio sounds in the error microphone signal, wherein the playback corrected error is based on a difference between the error microphone signal and the source audio signal and the modified playback corrected error signal is based on a difference between the playback corrected error signal and the scaled anti-noise signal.

Salt-and-pepper noise removal by adaptive median-based lifting filter using second-generation wavelets

Abstract: In this paper, we propose a novel adaptive median-based lifting filter for image de-noising which has been corrupted by homogeneous salt and pepper noise. The median-based lifting filter removes the noise of the input image by calculating the median of the neighboring significant pixels. The algorithm for image noise removal uses the lifting scheme of the second-generation wavelets in conjunction with the proposed adaptive median-based lifting filter. The experimental results demonstrate the efficiency of the proposed method. The proposed algorithm is compared with all the basic filters, and it is found that our method outperforms many other algorithms and it can remove salt and pepper noise with a noise level as high as 90%. The algorithm works exceedingly well for all levels of noise, as illustrated in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) measures.

Improving particle filters in rainfall-runoff models: Application of the resample-move step and the ensemble Gaussian particle filter

Abstract: [1] The objective of this paper is to analyze the improvement in the performance of the particle filter by including a resample-move step or by using a modified Gaussian particle filter Specifically, the standard particle filter structure is altered by the inclusion of the Markov chain Monte Carlo move step The second choice adopted in this study uses the moments of an ensemble Kalman filter analysis to define the importance density function within the Gaussian particle filter structure Both variants of the standard particle filter are used in the assimilation of densely sampled discharge records into a conceptual rainfall-runoff model The results indicate that the inclusion of the resample-move step in the standard particle filter and the use of an optimal importance density function in the Gaussian particle filter improve the effectiveness of particle filters Moreover, an optimization of the forecast ensemble used in this study allowed for a better performance of the modified Gaussian particle filter compared to the particle filter with resample-move step