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An Optimized Implementation of a Novel nonlinear filter for Color Image Restoration

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Intelligent Automation & Soft Computing 2023年第8期37卷 1553-1568页

作者： Turki M.Alanazi Department of Electrical Engineering College of EngineeringJouf UniversitySakakaSaudi Arabia

Image processing is becoming more popular because images are being used increasingly in medical diagnosis,biometric monitoring,and character *** these images are frequently contaminated with noise,which can corrupt subsequent image processing ***,in this paper,we propose a novel nonlinear filter for removing“salt and pepper”impulsive noise from a complex color *** new filter is called the Modified Vector Directional filter(MVDF).The suggested method is based on the traditional Vector Directional filter(VDF).However,before the candidate pixel is processed by the VDF,theMVDF employs a threshold and the neighboring pixels of the candidate pixel in a 3×3 filter window to determine whether it is noise-corrupted or *** reference color images corrupted by impulsive noise with intensities ranging from 3%to 20%are used to assess theMVDF’s *** results of the experiments show that theMVDF is better than the VDF and the Generalized VDF(GVDF)in terms of the PSNR(Peak Signal-to-Noise Ratio),NCD(Normalized Color Difference),and execution time for the denoised *** fact,the PSNR is increased by 6.554%and 12.624%,the NCD is decreased by 20.273%and 44.147%,and the execution time is reduced by approximately a factor of 3 for the MVDF relative to the VDF and GVDF,*** results prove the efficiency of the proposed ***,a hardware design is proposed for the MVDF using the High-Level Synthesis(HLS)flow in order to increase its *** design,which is implemented on the Xilinx ZynqXCZU9EG Field-ProgrammableGate Array(FPGA),allows the restoration of a 256×256-pixel image in 2 milliseconds(ms)only.

关键词： nonlinear filter impulsive noise noise reduction software/hardware optimization color image HLS FPGA

A New Method Based on Polytopic Linear Inclusion for nonlinear filter with Non-Gaussian Noise

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A New Method Based on Polytopic Linear Inclusion for Nonline...

Removing Random-Valued Impulse Noises by a Two-Staged nonlinear filtering Method

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作者： Bing Liu Zhen Chen Xiangdong Liu Fan Yang School of Automation Beijing Institute of Technology Key Laboratory for Intelligent Control & Decision of Complex Systems Beijing Institute of Technology

ISBN: (纸本)9781467355322

This paper presents a new method based on the polytopic linear differential inclusion and the robust mixed H2/∞ filtering for the design of the nonlinear filter with non-Gaussian noises. The main goal is to solve the problems of the complexity and large calculation number in the general nonlinear filter and the filtering design problem for systems with the non-Gaussian noises. The noises considered in the paper involve two different kinds of noises: white noises and energy bounded noises. Differing from the linearization in most nonlinear filters, the estimation error system for the nonlinear system is represented by an uncertain polytopic linear model, based on which, the rectification equations for the predicted errors are designed following the robust mixed H2/∞ filtering. The state estimates for the nonlinear system are given through updating the predictions by the rectified quantities, where, the state predicted quantities of the nonlinear system are gained by the prediction equation of the EKF. The evident advantage of the new filter is the filter coefficients of the rectification equation are constant, without the need to evaluate the Jacobian matrixes. As a result, the calculation number for the new filter is decreased much and the real-time performance of the new filter is much better than the EKF, though the accuracy is a little decline. Its effectiveness is demonstrated by using an example and the statistics result of the calculation number for the filters in the example.

关键词： nonlinear filter polytopic linear inclusion non-Gaussian noise

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Transactions of Nanjing University of Aeronautics and Astronautics 2016年第3期33卷 329-338页

作者： Ahmad Ashfaq Lu Yanting National Astronomical Observatories/Nanjing Institute of Astronomical Optics&Technology Chinese Academy of SciencesNanjing 210042P.R.China Key Laboratory of Astronomical Optics&Technology Nanjing Institute of Astronomical Optics&TechnologyChinese Academy of SciencesNanjing 210042P.R.China University of Chinese Academy of Sciences Beijing 100049P.R.China

Digital images are frequently contaminated by impulse noise(IN)during acquisition and *** removal of this noise from images is essential for their further *** this paper,a two-staged nonlinear filtering algorithm is proposed for removing random-valued impulse noise(RVIN)from digital *** pixels are identified and corrected in two cascaded *** statistics of two subsets of nearest neighbors are employed as the criterion for detecting noisy pixels in the first stage,while directional differences are adopted as the detector criterion in the second *** respective adaptive median values are taken as the replacement values for noisy pixels in each *** performance of the proposed method was compared with that of several existing *** experimental results show that the performance of the suggested algorithm is superior to those of the compared methods in terms of noise removal,edge preservation,and processing time.

关键词： image de-noising random-valued impulse noise nonlinear filter noisy pixel detection two-stage detection and correction method cascaded stages directional differences

Stochastic stability of cubature predictive filter

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Science China(Information Sciences) 2016年第9期59卷 107-130页

作者： Lu CAO Xiaoqian CHEN The State Key Laboratory of Astronautic Dynamics China Xi'an Satellite Control Center College of Aerospace Science and Engineering National University of Defense Technology

In this paper, the cubature predictive filter(CPF) is derived based on a third-degree spherical-radial cubature rule. It provides a set of cubature-points scaling linearly with the state-vector dimension, which makes it possible to numerically compute multivariate moment integrals encountered in the nonlinear predictive filter(PF). In order to facilitate the new method, the algorithm CPF is given firstly. Then, the theoretical analyses demonstrate that the estimated accuracy of the model error and system for the proposed CPF is higher than that of the traditional PF. Moreover, the authors analyze the stochastic boundedness and the error behavior of CPF for general nonlinear systems in a stochastic framework. In particular, the theoretical results present that the estimation error remains bounded and the covariance keeps stable if the system’s initial estimation error,disturbing noise terms as well as the model error are small enough, which is the core part of the CPF *** of the results have been demonstrated by numerical simulations for a nonlinear example system.

关键词： predictive filter cubature rule nonlinear filter stochastic system

Probabilistic data association algorithm based on ensemble Kalman filter with observation iterated update

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High Technology Letters 2015年第3期21卷 301-308页

作者：胡振涛 Fu Chunling Li Junwei Institute of Image Processing and Pattern Recognition Henan University College of Physics and Electronics Henan University

Aiming at improving the observation uncertainty caused by limited accuracy of sensors,and the uncertainty of observation source in clutters,through the dynamic combination of ensemble Kalman filter(EnKF) and probabilistic data association(PDA),a novel probabilistic data association algorithm based on ensemble Kalman filter with observation iterated update is ***,combining with the advantages of data assimilation handling observation uncertainty in EnKF,an observation iterated update strategy is used to realize optimization of EnKF in *** the object is to further improve state estimation precision of nonlinear ***,the above algorithm is introduced to the framework of PDA,and the object is to increase reliability and stability of candidate echo *** addition,in order to decrease computation complexity in the combination of improved EnKF and PDA,the maximum observation iterated update mechanism is applied to the iteration of ***,simulation results verify the feasibility and effectiveness of the proposed algorithm by a typical target tracking scene in clutters.

关键词： nonlinear filter observation iterated update ensemble Kalman filter (EnKF) probabilistic data association (PDA)

Application of Adaptive Reduced Sigma Points Unscented Kalman filter to the Tracking of Maneuvering Target

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Journal of Beijing Institute of Technology 2007年第1期16卷 74-77页

作者：周战馨陈家斌 School of Information Science and Technology Beijing institute of Technology Beijing 100081China

Based on the principle of statistical linear regression, a set of n ＋ 2 sigma points instead of 2n ＋ 1 sigma points used in the unscented Kalman filter （UKF）, is constructed to approximate the system state. And filter accuracy is second order. Real-time of modified UKF is improved. In order to describe accurately the maneuvering target, the ＂current＂ statistical model is used. And the equation of acceleration error covariance is modified at every sample time of the filter. The modified adaptive UKF is presented for estimating the position, velocity and acceleration of maneuvering target. Monte Carlo simulations show the modified adaptive UKF acquires good performance for tracking position of maneuvering target. The modified adaptive UKF has better computational efficiency than UKF.

关键词： nonlinear filter adaptive UKF reduced sigma point maneuvering target tracking

Marginalized particle filter for spacecraft attitude estimation from vector measurements

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控制理论与应用（英文版） 2007年第1期5卷 60-66页

作者： Yaqiu LIU Xueyuan JIANG Guangfu MA School of Astronautics Harbin Institute of Technology Harbin Heilongjiang 150001 China Information and Computer Engineering College Northeast Forestry University Harbin Heilongjiang 150040 China

An algorithm based on the marginalized particle filters （MPF） is given in details in this paper to solve the spacecraft attitude estimation problem： attitude and gyro bias estimation using the biased gyro and vector observations. In this algorithm, by marginalizing out the state appearing linearly in the spacecraft model, the Kalman filter is associated with each particle in order to reduce the size of the state space and computational burden. The distribution of attitude vector is approximated by a set of particles and estimated using particle filter, while the estimation of gyro bias is obtained for each one of the attitude particles by applying the Kalman filter. The efficiency of this modified MPF estimator is verified through numerical simulation of a fully actuated rigid body. For comparison, unscented Kalman filter （UKF） is also used to gauge the performance of MPE The results presented in this paper clearly derfionstrate that the MPF is superior to UKF in coping with the nonlinear model.

关键词： Attitude estimation Particle filter Spacecraft nonlinear filter Quaternion

Target tracking methods based on a signal-to-noise ratio model

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Frontiers of Information Technology & Electronic Engineering 2020年第12期21卷 1804-1814页

作者： Dai LIU Yong-bo ZHAO Zi-qiao YUAN Jie-tao LI Guo-ji CHEN National Lab of Radar Signal Processing Xidian UniversityXi'an 710071China Xi’an Electronic Engineering Research Institute Xi'an 710100China

In traditional target tracking methods,the angle error and range error are often measured by the empirical value,while observation noise is a *** this paper,the angle error and range error are *** are influenced by the signalto-noise ratio(SNR).Therefore,a model related to SNR has been established,in which the SNR information is applied for target *** with an advanced nonlinear filter method,the extended Kalman filter method based on the SNR model(SNR-EKF)and the unscented Kalman filter method based on the SNR model(SNR-UKF)are *** is little difference between the SNR-EKF and SNR-UKF methods in position precision,but the SNR-EKF method has advantages in computation time and the SNR-UKF method has advantages in velocity *** results show that target tracking methods based on the SNR model can greatly improve the tracking performance compared with traditional tracking *** target tracking accuracy and convergence speed of the proposed methods have significant improvements.

关键词： Signal-to-noise ratio(SNR)model Target tracking Angle error Range error nonlinear filter

Discrete-Time Sliding Mode filter with Parameter adaptation

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Discrete-Time Sliding Mode Filter with Parameter adaptation