In this correspondence, we review the backward-filtering algorithm, and give a compact proof of its validity using matrix notation. We will review the relation between backward filtering and off-line perceptual weight...
详细信息
In this correspondence, we review the backward-filtering algorithm, and give a compact proof of its validity using matrix notation. We will review the relation between backward filtering and off-line perceptual weighting in sparse-codebook CELP, and will show how a combination on-line/off-line parallel weighting algorithm can be used to reduce the search complexity of an overlapped sparse codebook by 30% to 50%.
A general formulation for developing a fast-block least-mean-square (LMS) adaptive algorithm is presented. In this algorithm, a convergence factor is obtained that is tailored for each adaptive filter coefficient and ...
详细信息
A general formulation for developing a fast-block least-mean-square (LMS) adaptive algorithm is presented. In this algorithm, a convergence factor is obtained that is tailored for each adaptive filter coefficient and is updated at each block iteration. These convergence factors are chosen to minimize the mean-squared error in the processed block and are easily computed from readily available signals. The algorithm is called the optimum block adaptive algorithm with individual adaptation of parameters (OBAI). It is shown that the new coefficient vector obtained from the OBAI algorithm is an estimate of the Wiener solution at each iteration. Implementation aspects of OBAI are examined and a technique is presented that eliminates matrix inversion by processing signals in overlapping blocks and applying the matrix inversion lemma. When the coefficients are updated once per input data sample, the resulting OBAI algorithm requires 7N/sup 2/-5N+9 multiplications and divisions (MAD) per iteration, where N is the number of estimated parameters. The convergence properties of OBAI are investigated and compared with several recently proposed algorithms.< >
In many applications, the constrained adaptive filtering algorithm has been widely studied. The classical constrained LMS algorithm is widely used because of its low computational complexity. However, the performance ...
详细信息
In many applications, the constrained adaptive filtering algorithm has been widely studied. The classical constrained LMS algorithm is widely used because of its low computational complexity. However, the performance of constrained LMS algorithm will degrade under correlated input or non-Gaussian noise. In order to overcome this defect, this brief proposes a constrained least mean M-estimation (CLMM) algorithm, which uses the M-estimation cost function for the constrained adaptive filter. Compared with the previous algorithms for non-Gaussian noise, such as constrained maximum correntropy criterion (CMCC) algorithm and constrained minimum error entropy (CMEE) algorithm, the proposed CLMM algorithm has lower computational complexity and better steady-state performance. In addition, the step-size range is determined by analyzing the mean square stability, which ensures the stability of the proposed CLMM algorithm. Simulation results illustrate that the proposed CLMM algorithm has better steady-state performance than previous algorithms in non-Gaussian noises with multi-peak distribution.
A new computationally efficient algorithm for sequential least-squares (LS) estimation is presented in this paper. This fast a posteriori error sequential technique (FAEST) requires 5p MADPR (multiplications and divis...
详细信息
A new computationally efficient algorithm for sequential least-squares (LS) estimation is presented in this paper. This fast a posteriori error sequential technique (FAEST) requires 5p MADPR (multiplications and divisions per recursion) for AR modeling and 7p MADPR for LS FIR filtering, where p is the number of estimated parameters. In contrast the well-known fast Kalman algorithm requires 8p MADPR for AR modeling and 10p MADPR for FIR filtering. The increased computational speed of the introduced algorithm stems from an alternative definition of the so-called Kalman gain vector, which takes better advantage of the relationships between forward and backward linear prediction.
In this brief, a robust constrained filtering algorithm is proposed by introducing a novel cost function framework into the constrained adaptive algorithm. The proposed algorithm is called the recursive constrained le...
详细信息
In this brief, a robust constrained filtering algorithm is proposed by introducing a novel cost function framework into the constrained adaptive algorithm. The proposed algorithm is called the recursive constrained least arctangent (RCLA) adaptive algorithm. Thanks to the robustness of arctangent function, the proposed RCLA algorithm shows superior convergence performance and better steady-state behavior against impulsive noises compared to other existing recursive methods. The mean square convergence analysis and theoretical transient mean square deviation (MSD) are derived in detail. Besides, to validate the theoretical analysis, the computer simulations are conducted to demonstrate the consistency between theoretical and simulated MSD results. Simulation results under non-Gaussian environments verify the superior behavior of the proposed RCLA algorithm compared to known algorithms.
Noise in television signals degrades both the image quality and the performance of image coding algorithms. This paper describes a nonlinear temporal filtering algorithm using motion compensation for reducing noise in...
详细信息
Noise in television signals degrades both the image quality and the performance of image coding algorithms. This paper describes a nonlinear temporal filtering algorithm using motion compensation for reducing noise in image sequences. A specific implementation for NTSC composite television signals is described, and simulation results on several video sequences are presented. This approach is shown to be successful in improving image quality and also improving the efficiency of subsequent image coding operations.
A new method for removing impulse noises from images is proposed. The filtering scheme is based on replacing the central pixel value by the generalized mean value of all pixels inside a sliding window. The concepts of...
详细信息
A new method for removing impulse noises from images is proposed. The filtering scheme is based on replacing the central pixel value by the generalized mean value of all pixels inside a sliding window. The concepts of thresholding and complementation which are shown to improve the performance of the generalized mean filter are introduced. The threshold is derived using a statistical theory. The actual performance of the proposed filter is compared with that of file commonly used median filter by filtering noise corrupted real images. The hardware complexity of the two types of filters are also compared indicating the advantages of the generalized mean filter.
An algorithm of distributed filtering using set models with confidence values is derived. No statistics of noise distribution are needed. The only information required is the sets with confidence values from which the...
详细信息
An algorithm of distributed filtering using set models with confidence values is derived. No statistics of noise distribution are needed. The only information required is the sets with confidence values from which the modeling and measurement errors and the initial values are obtained. Therefore, the algorithm has great potential for real-world applications.
A new numerical algorithm is presented which determines the coefficients of a low-pass nonequal-ripple modified Chebyshev function with multiplicity of the dominant root pair greater than one; as a result its degree i...
详细信息
A new numerical algorithm is presented which determines the coefficients of a low-pass nonequal-ripple modified Chebyshev function with multiplicity of the dominant root pair greater than one; as a result its degree is higher than the corresponding Chebyshev polynomial but a much lower dominant rootQ-factorQ_{c}is obtained. Intermediate modified Chebyshev functions with higher transition region attenuation and therefore increasedQ_{c}are also discussed.
This study presents a novel filtering methodology for Mobile Laser Scanning (MLS) data using robust iterative reweighting. Initially, 3D point clouds are projected onto a 2D grid to create surfaces from the lowest poi...
详细信息
This study presents a novel filtering methodology for Mobile Laser Scanning (MLS) data using robust iterative reweighting. Initially, 3D point clouds are projected onto a 2D grid to create surfaces from the lowest points. Weights are assigned based on the Height Above Ground (HAG) of these points. Ground points are distinguished by applying a surface function to the dataset via iterative reweighting. Among the tested four robust weight functions, the Denmark and Beaton-Tukey functions outperformed others, achieving total error values of 2.30 and 2.32 across three test areas, respectively. This method efficiently filters MLS data, irrespective of ground point proportions.
暂无评论