Recursive Median Filter Research Articles

A semantic edge-aware parameter efficient image filtering technique

The success of a structure preserving filtering technique has relied on its capability to recognize structures and textures present in the input image. In this paper a novel structure preserving filtering technique is presented that first, generates an edge-map of the input image by exploiting semantic information. Then, an edge-aware adaptive recursive median filter is utilized to produce the filter image. The technique provides satisfactory results for a wide variety of images with minimal fine-tuning of its parameters. Moreover, along with the various computer graphics applications the proposed technique also shows its robustness to incorporate spatial information for spectral-spatial classification of hyperspectral images. A MATLAB implementation of the proposed technique is available at-https://www.github.com/K-Pradhan/A-semantic-edge-aware-parameter-efficient-image-filtering-technique

1D recursive median filter based passive islanding detection strategy for grid‐connected distributed generations network

AbstractIn modern distribution networks, integration of distributed generations (DGs) at consumer premises is common. However, islanding detection in such a grid‐tied distributed generation (GTDG) network is the topmost challenge to ensure power quality and reliable operation. This paper presents a modified passive islanding detection scheme (MPIDS) for the GTDG network, using a One‐D recursive Median filter (ODRMF) algorithm. Firstly, at the point of common coupling (PCC), 3‐phase voltage is acquired. Next, the ODRMF is employed to extract 3rd harmonic and residuals from the acquired voltage signal. Afterward, two novel islanding detection indices developed are: (1) First change detection (FCD), which is calculated from the residuals, and (2) delta selected harmonic distortion (∆SHD), which is computed from 3rd harmonic content of the voltage signal. Finally, variations in the two indices are compared with the threshold to identify islanding. To validate the efficacy of the presented MPIDS, rigorous simulations in MATLAB/ Simulink are conducted on the UL‐1741 and IEEE 13‐bus GTDGs systems under different conditions. The results illustrate that the presented strategy is highly reliable in all assessed conditions and can detect the islanding as well as non‐islanding incidents effectively under balanced and unbalanced load/generation conditions with a very low non‐detection zone.

Harmonic Content-Based Protection Method for Microgrids via 1-Dimensional Recursive Median Filtering Algorithm

Microgrids (MGs) offers grid-connected (GC) and islanded (ID) operational modes. However, these dynamic modes of operation pose different microgrid protection challenges. This paper presents a new protection method for MGs based on a discrete one-dimensional recursive Median filter (1-DRMF). In the first step, the 1-DRMF is applied on a measured current signal on every single phase individually for targeted feature extraction. Then, the median filter deviation (MFD) and the selected harmonic distortion (SHD) are computed from the current signals of all phases independently. In the second step, the upsurges in the MFD and the SHD of all phases are cross-checked with the pre-established threshold value of 0.3 to identify and categorize fault incidents. Finally, the directional properties of three-phase (3-p) reactive energy are employed in order to pinpoint the faulty line section (LS). Many simulations were executed on MATLAB/Simulink to validate the sustainable performance of the established method. Results prove that the scheme can detect, classify, and locate the solid and high impedance faults (HIF) in the GC as well as the ID modes under radial and meshed scenarios.

An Efficient Steganalysis of Medical Images by Using Deep Learning Based Discrete Scalable Alex Net Convolutionary Neural Networks Classifier

Steganalysis is the technique that tries to beat steganography by detecting and removing secret information. Steganalysis involves the detection of a message embedded in a picture. Deep Learning (DL) advances have offered alternative approaches to many difficult issues, including the field of image steganalysis using deep-learning architecture based on convolutionary neural networks (CNN). In recent years, many CNN architectures have been established that have enhanced the exact identification of steganographic images. This work presents a novel architecture which involves a preprocessing stage using histogram equalization and adaptive recursive median filter banks to reduce image noise, a feature extraction stage using shearlet multilinear local embedding methods and then finally the classification can be done by using the discrete scalable Alex NET CNN classifier. Performance was evaluated on the RGB-BMP Steganalysis Dataset with different experimental setups. To prove the effectiveness of the suggested algorithm it can be compared with the other existing methodologies. This work improves classification accuracies on all other existing algorithms over test data.

A Field Programmable Gate Array (FPGA) Based Non-Linear Filters for Gas Turbine Prognostics

The removal of noise from signals obtained through the health monitoring systems in gas turbines is an important consideration for accurate prognostics. Several filters have been designed and tested for this purpose, and their performance analysis has been conducted. Linear filters are inefficient in the removal of outliers and noise because they cause smoothening of the sharp features in the signal which can indicate the onset of a fault event. On the other hand, non-linear filters based on image processing methods can provide more precise results for gas turbine health signals. Among others, the weighted recursive median (WRM) filter has been shown to provide greater accuracy due to its weight adaptability depending on the signal type. However, sampling data at high rates is possible which needs hardware implementation of the filter. In this paper, the design, simulation and implementation of WRM filters on the FPGA (Field Programmable Gate Arrays) platforms Vivado Design Suite by Xilinx and Quartus Pro Lite Edition 19.3 has been performed. The architectural detail and performance result with the FPGA filters when subjected to abrupt and gradual fault signal is presented.

Adaptive Four-dot Median Filter for Removing 1-99% Densities of Salt-and-Pepper Noise in Images

In order to accelerate denoising and improve the denoising performance of the current median filters, an Adaptive Four-dot Median Filter (AFMF) for image restoration is proposed in this article. AFMF is not only very efficient and fast in logic execution, but also it can restore the corrupted images with 1–99% densities of salt-and-pepper noise to the satisfactory ones. Without any complicated operation for noise detection, it intuitively and simply distinguishes impulse noises, while keeping the noise-free pixels intact. Only the uncorrupted pixels of the four-dot mask in adaptive filtering windows are used for the adoption of candidates for median finding, whatever filtering window size is. Furthermore, the adoption of recursive median filters leads to denoising performance improvement and faster filtering. The simple logic of the proposed algorithm obtains significant milestones on the fidelity of a restored image. Relevant experimental results on subjective visualization and objective digital measure validate the robustness of the proposed filter.

Denoising Signals Used in Gas Turbine Diagnostics with Ant Colony Optimized Weighted Recursive Median Filters

Accurate fault detection and isolation requires signal processing of measurement signals which are contaminated with noise. Typically, gas turbine faults are revealed by sharp trend shifts in the signals and these trend shifts should be preserved during signal processing. Linear filters can smooth out the sharp trend shifts while removing noise. However, nonlinear filters such as the weighted recursive median (WRM) filters show good noise reduction while preserving key signal features if their integer weights are determined optimally. We propose the ant colony optimization (ACO) method coupled with local search to calculate the integer weights of WRM filters. It is found that the filter weight optimization problem is mathematically equivalent to the quadratic assignment problem which can be solved by ACO. Optimal parameters for the ACO are found using numerical experiments. The WRM filter is demonstrated for abrupt and gradual faults in gas turbines and is found to yield noise reduction of 52–64% for simulated noisy signals considered in this paper.

Optimally weighted recursive median filters for denoising vibration signals used in health monitoring of rotating systems

A novel weighted recursive median (WRM) filter for denoising vibration signals is presented in this paper. Vibration signals emanating from rotating systems are typically periodic as the transients die out with time due to damping. The weights of this nonlinear filter are optimized for typical vibration signals encountered in helicopter main rotor vibration such as multi-frequency sinusoids. The resulting filter removes outliers and reduces noise to a considerable extent when compared to a baseline noisy signal. FFT results show that the WRM filter is able to perform the noise removal while causing no change to the underlying frequencies in the ideal signal. The integer design space for the filter is multimodal with several local minima and the filter design problem presents a benchmark problem for testing integer programming algorithms. An exhaustive search of the design space ensures that all the local minimum points are found and the global minimum is identified. The 5-point median filter with weights [2 1 2 1 2] is found to be good choice for denoising vibration signals. The WRM filter can be used as a data preprocessor before subjecting the vibration signals to fault detection and isolation algorithm.

Generalized Hampel Filters

The standard median filter based on a symmetric moving window has only one tuning parameter: the window width. Despite this limitation, this filter has proven extremely useful and has motivated a number of extensions: weighted median filters, recursive median filters, and various cascade structures. The Hampel filter is a member of the class of decsion filters that replaces the central value in the data window with the median if it lies far enough from the median to be deemed an outlier. This filter depends on both the window width and an additional tuning parameter t, reducing to the median filter when t=0, so it may be regarded as another median filter extension. This paper adopts this view, defining and exploring the class of generalized Hampel filters obtained by applying the median filter extensions listed above: weighted Hampel filters, recursive Hampel filters, and their cascades. An important concept introduced here is that of an implosion sequence, a signal for which generalized Hampel filter performance is independent of the threshold parameter t. These sequences are important because the added flexibility of the generalized Hampel filters offers no practical advantage for implosion sequences. Partial characterization results are presented for these sequences, as are useful relationships between root sequences for generalized Hampel filters and their median-based counterparts. To illustrate the performance of this filter class, two examples are considered: one is simulation-based, providing a basis for quantitative evaluation of signal recovery performance as a function of t, while the other is a sequence of monthly Italian industrial production index values that exhibits glaring outliers.

Recursive Median Filter for the Background Estimation and Foreground Segmentation in Surveillance Videos

Video cameras are widely used in surveillance systems; this offers the possibility of processing the captured images for automatic detection of events of interest that may arise in the scene. The present paper proposes a method for estimating the background and foreground segmentation in video surveillance using a recursive median filter and applying a temporal moving window in the number of frames to be analyzed, which provide more robustness against noise caused by changes in illumination and camera shake, limiting the increase in the computational cost of processing.

Identification, Analysis, and Elimination of Craquelures in Old Oil Painting Images

In this study, an integrated approach to identifying, analyzing and eliminating craquelures in high resolution oil paintings in images is proposed. It starts by identifying the craquelure networks in the oil painting via utilizing morphological top-hat operator, and follows by running a newly-devised crack line tracing algorithm to obtain properties of all the individual crack line segment such as the length and orientation. Closed regions in the craquelure netowrks is subsequently searched to extract further information about the craquelure patterns. Then, an improved recursive median filters is proposed to carry out the virtual restoration of the oil painting. The study concludes by presenting the experimental results which demonstrate good performance on oil painting craquelure analysis and virtual restoration.

An Improved De-Noising Algorithm for Highly Corrupted Color Videos Using FPGA Based Impulse Noise Detection and Correction Techniques

Objective : This research focuses on design and implementation of FPGA hardware architecture based image de-noising algorithm with automatic detection and correction of impulse noise. With the proposed hardware, image or video camera can be interfaced with the implementing hardware for de-noising of salt and pepper noise called impulse noise. Methods/Analysis : The algorithms proposed in this research mainly work on images to identify the impulse noise affected pixel and correct only the corrupted pixel instead of uncorrupted. Also the novelty method modifies the existed features of various de-noising techniques using real time, low power FPGA architecture for the noise detection and correction. Findings : The proposed research is in two stages namely modified boundary discriminative noise detection and recursive median filter based correction technique. These two stages are tested for various noise densities and delivered better de-noising factor than the existing algorithms. The simulation results of the implemented algorithm prove efficiency of 98% noise reduction factor for 90% corrupted image or video.

An Enhanced Recursive Median Filter for Noise Reduction based on Randomness in Pixel Values of an Image

is a prominent factor which when added in an image reduces its quality. So to enhance the quality of an image, removing noise is required which is an important field of research. Many researchers have proposed various algorithms and compared their result. This paper focuses on removing noise from distorted image using Recursive Median Filter. Recursive Median Filter (RMF) is one of the noise reduction technique used for removing impulse noise. To enhance the performance of RMF using the concept of degree of randomness i.e. Entropy, a novel approach is introduced named as Enhanced Recursive Median Filter (ERMF). In the proposed filter, the size of moving window is changed based on the entropy value of moving window. Result shows that the proposed filter enhance the performance in removing the noise as compared to recursive median filter.

Removal of Impulse Noise using Iterative Unsymmetrical Trimmed Median Filter

is universally accepted that Median filter is the best filter known so far. Based on this fact many variants of median filter were developed to improve the performance of the standard median filter. In this paper a new approach for the restoration of gray scale and color images that are highly corrupted by impulse noise is proposed. The algorithm works on low density noise also. The algorithm has three stages - firstly, finding the corrupted pixels, secondly de-noising the corrupted pixels; thirdly, minimizing the de-noised image to root image. The article proves that the new approach is guaranteed to converge to root image within a finite number of iterations. The proposed algorithm shows better results than the Standard Median Filter, Recursive Median Filter and Decision based Unsymmetrical Trimmed Median Filter.

Fault Detection and Isolation in Inertial Measurement Units Based on -CUSUM and Wavelet Packet

The aim of this paper is to present a fault detection algorithm (FDI) based on signal processing techniques developed for an inertial measurement unit (IMU) with minimal redundancy of fiber optic gyros. In this work the recursive median filter is applied in order to remove impulses (outliers) arising from data acquisition process and parity vector operations, improving the fault detection and isolation performance. The FDI algorithm is divided into two blocks: fault detection (FD) and fault isolation (FI). The FD part of the algorithm is used to guarantee the reliability of the isolation part and is based on parity vector analysis using -CUSUM algorithm. The FI part is performed using parity space projection of the energy subbands obtained from wavelet packet decomposition. This projection is an extension of clustering analysis based on singular value decomposition (SVD) and principal component analysis (PCA). The results of the FD and FI algorithms have shown the effectiveness of the proposed method, in which the FD algorithm is capable of indicating the low-level step bias fault with short delay and a high index of correct decisions of the FI algorithm also with low-level step bias fault.

Three- and Seven-Point Optimally Weighted Recursive Median Filters for Gas Turbine Diagnostics

Measured health signals incorporate significant details about any malfunction in a gas turbine. The attenuation of noise and removal of outliers from these health signals while preserving important features is an important problem in gas turbine diagnostics. The measured health signals are a time series of sensor measurements such as the low rotor speed, high rotor speed, fuel flow, and exhaust gas temperature in a gas turbine. In this article, a comparative study is done by varying the window length of acausal and unsymmetrical weighted recursive median filters and numerical results for error minimization are obtained. It is found that optimal filters exist, which can be used for engines where data are available slowly (three-point filter) and rapidly (seven-point filter). These smoothing filters are proposed as preprocessors of measurement delta signals before subjecting them to fault detection and isolation algorithms.

Recursive polynomial weighted median filtering

In the paper, the recursive polynomial weighted median (RPWM) filtering is introduced. The derivation of a technique is based on the recursive polynomial and recursive median filtering. The specific case of the second-order of recursive polynomial weighted median filter was discussed in detail. The adaptive equations for the filter coefficients were also presented. It was tested that a specific unknown nonlinear system can better approximated with a RPWM filter than with a polynomial weighted median (PWM) one and where the RPWM filter output is more stable for an impulse like input signal interference. The equations for the adaptation of the nonnegative integer WM subfilters in a RPWM filter were also given.

Deinterlacing Using Hierarchical Motion Analysis

A motion-compensated deinterlacing scheme based on hierarchical motion analysis is presented. According to deinterlacing steps, our contribution can be divided into four parts: motion estimation, motion state analysis, motion consistency analysis, and finer-grained interpolation. In motion estimation, we introduce a Gaussian noise model for choosing the best motion vector for each block, and make a tradeoff between utilizing previous de-interlaced frames and avoiding error propagation. A directional interpolation method is also introduced in this part for backward fields. In motion state analysis, we define two motion states for each pixel, thus achieve a compromise between traditional block-based strategies and the extreme pixel-based case. In motion consistency analysis, we propose to measure both the motion vector consistency and the motion state consistency in order to determine whether the previous two parts should be performed again with a different block size. In finer-grained interpolation, we utilize a combination of recursive median filters to generate the final results. Experimental results show that all of the proposed techniques are effective, either objectively or subjectively. As a result, we can achieve much higher image quality, with an average gain of about 1.83 dB in terms of peak signal-to-noise ratio. Moreover, the increased computation complexity is marginal.

Jet Engine Health Signal Denoising Using Optimally Weighted Recursive Median Filters

The removal of noise and outliers from health signals is an important problem in jet engine health monitoring. Typically, health signals are time series of damage indicators, which can be sensor measurements or features derived from such measurements. Sharp or sudden changes in health signals can represent abrupt faults and long term deterioration in the system is typical of gradual faults. Simple linear filters tend to smooth out the sharp trend shifts in jet engine signals and are also not good for outlier removal. We propose new optimally designed nonlinear weighted recursive median filters for noise removal from typical health signals of jet engines. Signals for abrupt and gradual faults and with transient data are considered. Numerical results are obtained for a jet engine and show that preprocessing of health signals using the proposed filter significantly removes Gaussian noise and outliers and could therefore greatly improve the accuracy of diagnostic systems.

FLANN-based adaptive threshold selection for detection of impulsive noise in images

In this paper, a novel scheme has been suggested for removing random-valued impulsive noise from images. The proposed scheme utilizes a second-order differential impulse detection followed by a recursive median filter on the corrupted pixel locations. Adaptive threshold selection from noisy image characteristics has been emphasized in this paper. A functional link artificial neural network is used for this purpose. Comparative analysis on standard images at different noise conditions shows that the proposed scheme, in general, outperforms the existing schemes.