Fast Adaptive Filtering Research Articles

A random exploration based fast adaptive and selective mean filter for salt and pepper noise removal in satellite digital images

The digital image is one of the discoveries that play an important role in various aspects of modern human life. These findings are useful in various fields, including defense (military and non-military), security, health, education, and others. In practice, the image acquisition process often suffers from problems, both in the process of capturing and transmitting images. Among the problems is the appearance of noise which results in the degradation of information in the image and thus disrupts further processes of image processing. One type of noise that damages digital images is salt and pepper noise which randomly changes the pixel values to 0 (black) or 255 (white). Researchers have proposed several methods to deal with this type of noise, including median filter, adaptive mean filter, switching median filter, modified decision based unsymmetric trimmed median filter, and different applied median filter. However, this method suffers from a decrease in performance when applied to images with high-intensity noise. Therefore, in this research, a new filtering method is proposed that can improve the image by randomly exploring pixels, then collecting the surrounding pixel data from the processed pixels (kernel). The kernel will be enlarged if there are no free-noise pixels in the kernel. Furthermore, the damaged pixels will be replaced using the mean data centering statistic. Images enhanced using the proposed method have better quality than the previous methods, both quantitatively (SSIM and PSNR) and qualitatively.

FPGA implementation of hybrid recursive reversable box filter-based fast adaptive bilateral filter for image denoising

A Bilateral filter (BF) is a kind of edge-preserving filter, which is mostly used in computer vision, computational photography, and image processing applications. Recently, Reversible Logic Gates (RLG) technology has emerged as an attractive area in the field of field-programmable gate array (FPGA) based implementations. The RLG technology consumes less Quantum Cost (QC) as compared to conventional CMOS-based logic gates. As a result, this paper proposed an FPGA implementation of a hybrid recursive reversible box filter (HRRBF)-based fast adaptive bilateral filter (FABF) using RLG technology with joint geometric and photometric weight calculation and kernel result calculation operations. The RLGs are used to develop the reversable modified carry select adder (RMCSA), reversable adder subtractor (RAS), and multiplier, which significantly improves the speed and reduces the execution time, area, power, and delay as compared to the basic methods. In addition, ternary content addressable memory (TCAM) is used as the main memory to improve the speed of filtering. Obtained hardware and simulation results show that proposed RLG-based HRRBF-FABF results in enhanced performance as compared to state-of-the-art architectures. In addition, the image denoising application was also implemented with the proposed RLG-based HRRBF-FABF and achieved structural similarity (SSIM) index of 0.999 and peak signal-to-noise ratio (PSNR) of 78.348 dB. The simulation results showed that the proposed method resulted in better denoising performance as compared to conventional filtering approaches.

Combined adaptive antenna array and adaptive channel equalizer, based on multichannel fast recursive least squares algorithms

An adaptive antenna array (AAA) is a sort of directional antennas, which is able to suppress the signals of the interference sources. The AAA works well if there is no multipath propagation of the information signal. A device, which removes the multipath effects, is an adaptive equalizer. However, the independent AAA and equalizer do not work well if the interferences and multipath exist simultaneously. The purpose of this paper is to present a new efficient solution of the task by using of the AAA, whose weights are shared with the weights of the fractionally-spaced feed-forward (FF) part of the equalizer. The array output signal is combined with that of the feed-backward (FB) part of the equalizer. The array and such distributed equalizer operate as an indivisible adaptive filter, providing good performance and ability to receive the multipath information signals in the presence of the signals of the external interference sources. Because the FF part of the equalizer operates at the up-sampled rate, which is a few times higher than the information symbols rate, but the FB filter operates at the symbol rate, the computationally efficient fast recursive least squares (RLS) algorithms cannot be used directly in the considered adaptive unit. To solve this problem, it is proposed to use polyphase representation of the FF part of the equalizer. In this case, the architecture of the adaptive unit is viewed as a multichannel adaptive filter with unequal number of weights in channels. The paper presents the architecture of the adaptive unit and the details of its equalizer part. The mathematical description of a multichannel fast adaptive filtering algorithm, called stabilized fast a posteriori sequential technique (FAEST), fitted for the weights calculation of the considered unit is also presented. The verification of the proposed adaptive unit has been carried out via simulation, using a linear array with sixteen omnidirectional antennas, which operates in the conditions of –30 dB signal-to-interference ratio (SIR), 10…30 dB signal-to-noise ratio (SNR), receives the phase shift keying (8-PSK) desired signal, passed through a two-rays communication channel with about –65 dB gaps in the channel amplitude-frequency response. In the steady-state, the proposed adaptive unit demonstrates about –100 dB radiation pattern gaps towards the interference sources and the transient response with less than 500 symbols duration. The simulation demonstrates the ability of the proposed unit simultaneously to steer the main lobe of antenna array radiation pattern towards the desired signal source without a priori search of its angular location and to remove the signals of the external interferences and intersymbol interference in the array output signal. Due to these properties, the proposed AAA/equalizer can be used in equipment of modern mobile communication systems.

On the performance of all-optical OFDM based PM-QPSK and PM-16QAM

A polarization multiplexed all-optical orthogonal frequency division multiplexing (AO-OFDM) system is investigated analytically and numerically, with 12.5 Gbaud symbol rate, that is compatible with the International Telecommunication Union flexible grid. We demonstrate that fast adaptive filtering is not require at the receiver and chromatic dispersion with polarization mode dispersion can be compensated without the cyclic prefix insertion, with the maximum spectral efficiency by using proper optical filter. System performance are numerically and analytically evaluated considering nonlinear effects and power consumption, evidencing a good agreement between the theoretical model and numerical results. We compare AO-OFDM performance with coherent OFDM scheme using the derived theoretical formula and numerical results. 21 subcarriers are investigated with quadrature phase shift keying modulation and 7 subcarriers in case of 16-quadrature amplitude modulation, with a total capacity of 976.5 Gb/s and 651 Gb/s, respectively, considering 7% forward error correction overhead.

Deep Learning Based Switching Filter for Impulsive Noise Removal in Color Images.

Noise reduction is one of the most important and still active research topics in low-level image processing due to its high impact on object detection and scene understanding for computer vision systems. Recently, we observed a substantially increased interest in the application of deep learning algorithms. Many computer vision systems use them, due to their impressive capability of feature extraction and classification. While these methods have also been successfully applied in image denoising, significantly improving its performance, most of the proposed approaches were designed for Gaussian noise suppression. In this paper, we present a switching filtering technique intended for impulsive noise removal using deep learning. In the proposed method, the distorted pixels are detected using a deep neural network architecture and restored with the fast adaptive mean filter. The performed experiments show that the proposed approach is superior to the state-of-the-art filters designed for impulsive noise removal in color digital images.

Эквалайзеры с дробной задержкой и обратной связью на базе быстрых RLS-алгоритмов

Эквалайзеры с дробной задержкой и обратной связью на базе быстрых RLS-алгоритмов

Impulse Noise Removal in Mammograms using Bi-Dimensional Empirical Mode Decomposition and Fast Adaptive Bilateral Filter

The work aims to detect and correct the noisy mammogram images corrupted by impulse noise. This is achieved in two phases – identification of noise-affected pixels and renovation of those pixels in an image. The pixels which are disturbed by impulse noise are identified by Bi-dimensional Empirical Mode Decomposition (Bid-EMD). The restoration of these pixels and noise removal are done by fast adaptive bilateral filter (fABF). The proposed work for impulse noise removal is examined on digital mammogram images of Digital Database for Screening Mammography (DDSM) database. The proposed approach is compared with other existing state-of-the-art schemes using peak signal to noise ratio (PSNR) and image enhancement factor (IEF) performance measures. The study of performance of the proposed scheme provides enhanced outcome than the other algorithms used for impulse noise removal.

A fast adaptive resonance suppression scheme for high-damping system using R-SDFT detection

ABSTRACTAdaptive notch filter resonance suppression scheme is widely used in high-performance servo drivers. For high-damping system, the deviation between resonant and oscillation frequency will cause a long sustained vibration in traditional suppression schemes, which may damage the precision machine irreparably. To reduce the harm of sustained vibration, a fast adaptive notch filter scheme based on range-shifted discrete Fourier transform (R-SDFT) is proposed. Different from the traditional scheme, the new one eliminates the deviation between resonant and oscillation frequency by notch filter other than the low-pass filter, which massively simplifies the suppression process and reduces the number of oscillation frequency detection required. Meanwhile, an oscillation detection method based on R-SDFT is presented to shorten the time of the detective process through interval detection and prediction strategy. Compared with the traditional strategy, the experimental results demonstrate that the proposed adaptive suppression scheme can reduce the oscillation time by more than half.

Current control based power management strategy for distributed power generation system

The integration of renewable energy sources (RES) based distributed generation (DG) systems into electric grid has many challenges such as synchronization, control, power management (PM) and power quality problems. This paper proposes a references current generator (RCG) based PM strategy to control three phase inverter and manage power flow among the DG energy sources, electric grid and load demand under balanced and unbalanced grid conditions. The amplitudes of active and reactive power oscillations are also eliminated and controlled through only one flexible control parameter (FCP) under grid faults and harmonic distortions. Compared with previous similar studies, one of the important contributions is capable to inject maximum active power and minimum reactive power into electric grid and load at inverter power capacity under grid faults Another contribution is to extract the positive and negative sequence (PNS) voltage and current components with the improved fast and robust dual adaptive filters based phase locked loop (DAF-PLL). Fractional order proportional integral (FOPI) is selected as an attractive solution for AC current regulation to exhibit fast transient response and to achieve zero steady-state errors as compared with conventional current regulation controllers in synchronous or stationary frames.

Fast adaptive and selective mean filter for the removal of high‐density salt and pepper noise

A fast adaptive and selective mean filter is presented to remove salt and pepper noise effectively from images corrupted with higher noise densities. The algorithm achieves better results in terms of visual quality and in terms of peak signal-to-noise ratio, mean absolute error, mean structural similarity index measure, image enhancement factor, and edge preservation ratio than many existing state-of-the-art algorithms at all noise densities. Adaptive filters that use variable window size produce better restoration of salt and pepper noise at higher noise densities than filters that use fixed window size, but they consume more time. This makes them practically impossible to implement them in digital image acquisition devices. Hence, reducing the execution time of adaptive filters is vital. The proposed algorithm consumes around 90% less time for lower noise densities and 50% less time for higher noise densities than the adaptive weighted mean filter, one of the best available adaptive filters in the literature for high-density salt and pepper noise removal.

SIMO-OFDM Cellular Network: Multiuser Interference Mitigation and Desired User Identification

The rapidly increasing demand for mobile broadband traffic drives the need for even more capacity from cellular networks. Multiple Input Multiple Output (MIMO) technology is a key element of modern wireless communication standard including Long Term Evolution (LTE) and WiMAX. MIMO systems are used to improve the robustness of data transmission, to increase data rate, to improve capacity and other aspects of network performance. In the absence of line of sight (LOS) between the mobile receiver station (MS) and the base station (BS), the localization of the BS is very important. TheDirection of Arrival (DOA) algorithms can estimate the incident angles of all the received signals impinging on the array. These algorithms give the DOAs of all the relevant signals of the user sources and interference sources. Yet, they are not capable of distinguish and identify which one is the direction of the desired user. In this paper, a reference signal is known by the transmitter and the receiver is proposed to identify which one of the estimated DOAs is the direction of the desired user in the cell. With the aid of such reference signal and applying the correlation concept, the DOA algorithmscan distinguish the direction of the desired signal from the others. This concept was applied to the MUltiple SIgnal Classification (MUSIC), ROOT MUSIC, and Minimum Variance Distortion less Response (MVDR) algorithms. Moreover, the Affine Projection Algorithm (APA) is classified as a fast adaptive filter was implemented in this paper to enhance the accuracy of the estimated direction and to form a beam towards the desired user and nulls towards interferes. Extensive simulation results assure that in the presence of the proposed reference signal, the DOAs algorithms can identify the direction of the desired user with high accuracy and resolution.

Heart Rate Monitoring Using a Slow–Fast Adaptive Comb Filter to Eliminate Motion Artifacts

This paper proposes an algorithm for eliminating motion artifacts in heart rate monitoring using a photoplethysmography (PPG) sensor. No additional hardware, such as accelerometers or other reference signals, is needed. The algorithm improves the quality of PPG signals by implementing a comb filter during signal processing. The proposed slow–fast adaptive comb filter (SFACF) distinguishes motion artifacts in PPG signals based on harmonic and continuous features of the heart rate. The effectiveness of fundamental and harmonic frequency enhancement in real time is experimentally demonstrated, with the heart rate updated every second. The performance of the SFACF, which does not require additional reference signals, is compared with those of adaptive noise cancellation (ANC) and spectral peak search-comb filtering (SPS-CF). It is found that for a low-quality PPG signal, the SFACF outperforms ANC and SPS-CF. Finally, measurements obtained using a commercial heart rate monitor and the proposed algorithm are shown to be highly correlated.

An extremely fast adaptive high-performance filter to remove salt and pepper noise using overlapping medians in images

A new fast adaptive high-performance filter (FAHPF) has been proposed to remove salt-and-pepper noise in images. ‘Maximize the speed without compromising denoising performance’ is the fundamental intention to build up the FAHPF algorithm. Among diverse phases of filtering employed in the FAHPF, overlapping medians (OM) is our newly proposed frame-based filtering concept which is the basis for speed of FAHPF and running averages embedded with OM is an idea behind excellent denoising performance of FAHPF at the same pace. Simulation experiments have been conducted and denoising results of FAHPF has been investigated against very recently developed filtering methods. It is proved that the FAHPF excellently outperforms many of state-of-the-art filters considered for comparison, in terms of peak signal to noise ratio, structural similarity index, and visual representation and requires the extremely shortest execution time among all, which could make it as a real time filter.

Histogram-Based Fast Adaptive Bilateral Filter for Ultrasound Speckle Reduction

Tissue structure including lesions in ultrasound images is important in clinical diagnosis. The way to suppress noise effectively and to preserve the structure is vital for diagnosis and also for image postprocessing. In order to reduce speckle and improve the quality of ultrasound images, this paper presents a fast adaptive bilateral filter (FABF) based on local histogram. The presented filter is derived from a conventional bilateral filter and is adaptively adjusted by speckle detection based on histogram matching. The criterion of speckle region is defined from a similarity value obtained from local histogram matching between the processing window and a reference speckle area. The presented filter can effectively reduce the speckle noise and, at the same time, maintain the tissue structure. In phantom and in vivo data, the proposed method can improve the quality of an ultrasound image in terms of tissue SNR and CNR values.

Filter Window Characteristics Based Fast Adaptive Median Filter

This paper describes a Filter Window Characteristics based fast adaptive median filter algorithm. Experimental results from the point of view, this adaptive median filter algorithm to determine the degree of pollution on the image to adjust adaptive filtering process, while maintaining the average speed median filter algorithm based on filtering noise effectively, and better protection of the image the edge of the details.

Fast super-resolution with affine motion using an adaptive Wiener filter and its application to airborne imaging

Fast nonuniform interpolation based super-resolution (SR) has traditionally been limited to applications with translational interframe motion. This is in part because such methods are based on an underlying assumption that the warping and blurring components in the observation model commute. For translational motion this is the case, but it is not true in general. This presents a problem for applications such as airborne imaging where translation may be insufficient. Here we present a new Fourier domain analysis to show that, for many image systems, an affine warping model with limited zoom and shear approximately commutes with the point spread function when diffraction effects are modeled. Based on this important result, we present a new fast adaptive Wiener filter (AWF) SR algorithm for non-translational motion and study its performance with affine motion. The fast AWF SR method employs a new smart observation window that allows us to precompute all the needed filter weights for any type of motion without sacrificing much of the full performance of the AWF. We evaluate the proposed algorithm using simulated data and real infrared airborne imagery that contains a thermal resolution target allowing for objective resolution analysis.

The perception of suprathreshold contrast and fast adaptive filtering

We examine how the perceived contrast of dynamic noise images depends upon temporal frequency (TF) and mean luminance. A novel stepwise suprathreshold matching paradigm shows that both threshold and suprathreshold contrast sensitivity functions may be described by an inverted-U shape as a function of TF. The shape and the peak TF of the tuning function vary with the conditions under which it is measured. Spatiotemporal vision is weakly band-pass at low luminance levels (0.8 cd/m(2)) but becomes more strongly band-pass at high luminances (40-400 cd/m(2)). The peak temporal frequencies of the band-pass functions increase with the mean luminance and contrast of the test signals. As a function of increasing image contrast, our results demonstrate that the visual system broadens the spatiotemporal bandwidth of its signal detection mechanisms, especially at high mean luminances. Our results are shown to be consistent with an adaptable signal transmission system in which early luminance-dependent gain control mechanisms, in combination with on-line estimates of contrast via the autocorrelation function lead to an adaptive enhancement of spatiotemporal vision at high temporal frequencies.

Study of a new fast adaptive filtering algorithm

A new fast adaptive filtering algorithm was presented by using the correlations between the signal’s former and latter sampling times. The proof of the new algorithm was also presented, which showed that its optimal weight vector was the solution of generalized Wiener equation. The new algorithm was of simple structure, fast convergence, and less stable maladjustment. It can handle many signals including both uncorrelated signal and strong correlation signal. However, its computational complexity was comparable to that of the normalized least-mean-square (NLMS) algorithm. Simulation results show that for uncorrelated signals, the stable maladjustment of the proposed algorithm is less than that of the VS-NLMS algorithm, and its convergence is comparable to that of the algorithm proposed in references but faster than that of L.E-LMS algorithm. For strong correlation signal, its performance is superior to those of the NLMS algorithm and DCR-LMS algorithm.

A numerically stable fast newton-type adaptive filter based on order recursive least squares algorithm

This paper presents a numerically stable fast Newton-type adaptive filter algorithm. Two problems are dealt with in the paper. First, we derive the proposed algorithm from an order-recursive least squares algorithm. The result of the proposed algorithm is equivalent to that of the fast Newton transversal filter (FNTF) algorithm. However, the derivation process is different. Instead of extending a covariance matrix of the input based on the min-max and the max-min criteria, the derivation shown in this paper is to solve an optimum extension problem of the gain vector based on the information of the Mth-order forward or backward predictor. The derivation provides an intuitive explanation of the FNTF algorithm, which may be easier to understand. Second, we present stability analysis of the proposed algorithm using a linear time-variant state-space method. We show that the proposed algorithm has a well-analyzable stability structure, which is indicated by a transition matrix. The eigenvalues of the ensemble average of the transition matrix are proved all to be asymptotically less than unity. This results in a much-improved numerical performance of the proposed algorithm compared with the combination of the stabilized fast recursive least squares (SFRLS) and the FNTF algorithms. Computer simulations implemented by using a finite-precision arithmetic have confirmed the validity of our analysis.

Fast QR Algorithms Based on Backward Prediction Errors: A New Implementation and Its Finite Precision Performance

QR decomposition techniques are well known for their good numerical behavior and low complexity. Fast QRD recursive least squares adaptive algorithms benefit from these characteristics to offer robust and fast adaptive filters. This paper examines two different versions of the fast QR algorithm based on a priori backward prediction errors as well as two other corresponding versions of the fast QR algorithm based on a poste- riori backward prediction errors. The main matrix equations are presented with different versions derived from two distinct deployments of a particular matrix equation. From this study, a new algorithm is derived. The discussed algorithms are compared, and differences in computational complexity and in finite-precision behavior are shown. Ke yw ords: Adaptive system, fast RLS algorithm, QR decomposition.