Trimmed Mean Filter Research Articles

Seismic Data Enhancement Based on Common-Reflection-Surface-Based Local Slope and Trimmed Mean Filter

Seismic data often contain noise that can disturb or mask effective information. Noise elimination is an important and challenging task in seismic signal processing. Considering the high amplitude continuity of seismic events in the shot domain, this article proposes a structure-oriented denoising method that can enhance the effective events and suppress disturbing noise, including both incoherent and coherent noise. Based on the common-reflection-surface (CRS) travel time, the local slope of seismic events in the shot domain is deduced and estimated to provide structural information for plane-wave prediction. The proposed CRS-based slope depends on fewer parameters (two in 2D) than the conventional full CRS travel time (three in 2D), making it computationally efficient. Using the local slope, the third dimension is created using the plane-wave differential equation to predict the current trace from its neighbor traces and trimmed mean filtering (TMF) is applied in this dimension. The added dimension can be regarded as flattening the seismic events within a neighboring window and collapsing after application of the TMF. Synthetic and field datasets are employed to demonstrate the effectiveness of the proposed structure-oriented TMF. Compared with wavelet and plane-wave destruction methods, the proposed method can preserve more useful information with greater continuity in amplitude.

Enhanced Detection of Cerebral Atherosclerosis Using Hybrid Algorithm of Image Segmentation

In medical science for envisaging human body’s phenomenal structure a major part has been driven by image processing techniques. Major objective of this work is to detect of cerebral atherosclerosis for image segmentation application. Detection of some abnormal structures in human body has become a difficult task to complete with some simple images. For expounding and distinguishing neural architecture of human brain in an effective manner, MRI (Magnetic Resonance Imaging) is one of the most suitable and significant technique. Here we work on detection of Cerebral Atherosclerosis from MRI images of patients. Cerebral Atherosclerosis is a cerebral vascular disease causes narrowing of the arteries due to buildup of fatty plaque inside the blood vessels of the brain. It leads to Ischemic stroke if not diagnosed early. Stroke affects majorly old age people and percentage of affected women is more compared to men. Results: Preprocessing is done by using alpha trimmed mean filter which is used to remove noise and also it enhances the image. Segmentation of cerebral atherosclerosis is done by using K-means clustering, Contextual clustering, and proposed Hybrid algorithm. Various parameters like Correlation, Pixel density, energy is determined and from the analysis of parameters it is determined that proposed Hybrid algorithm is efficient.

A Precise Computational Method for Hippocampus Segmentation from MRI of Brain to Assist Physicians in the Diagnosis of Alzheimer’s Disease

Hippocampus segmentation on magnetic resonance imaging is more significant for diagnosis, treatment and analyzing of neuropsychiatric disorders. Automatic segmentation is an active research field. Previous state-of-the-art hippocampus segmentation methods train their methods on healthy or Alzheimer’s disease patients from public datasets. It arises the question whether these methods are capable for recognizing the hippocampus in a different domain. Therefore, this study proposes a precise computational method for hippocampus segmentation from MRI of brain to assist physicians in the diagnosis of Alzheimer’s disease (HCS-MRI-DAD-LBP). Initially, the input images are pre-processed by Trimmed mean filter for image quality enhancement. Then the pre-processed images are given to ROI detection, ROI detection utilizes Weber’s law which determines the luminance factor of the image. In the region extraction process, Chan–Vese active contour model (ACM) and level sets are used (UACM). Finally, local binary pattern (LBP) is utilized to remove the erroneous pixel that maximizes the segmentation accuracy. The proposed model is implemented in MATLAB, and its performance is analyzed with performance metrics, like precision, recall, mean, variance, standard deviation and disc similarity coefficient. The proposed HCS-MRI-DAD-LBP method attains in OASIS dataset provides high disc similarity coefficient of 12.64%, 10.11% and 1.03% compared with the existing methods, like HCS-DAS-MLT, HCS-DAS-RNN and HCS-DAS-GMM and in ADNI dataset provides high precision of 20%, 9.09% and 1.05% compared with existing methods like HCS-MRI-DAD-CNN-ADNI, HCS-MRI-DAD-MCNN-ADNI and HCS-MRI-DAD-CNN-RNN-ADNI, respectively.

Modified decision based unsymmetric adaptive neighborhood trimmed mean filter for removal of very high density salt and pepper noise

In this paper, a novel and efficient algorithm, Modified Decision Based Unsymmetric Adaptive Neighborhood Trimmed Mean Filter, for removal of very high density salt and pepper noise (SPN) is proposed. The proposed technique comprises of two phases. The first phase involves the fusion of decision based partial trimmed global mean filter, decision based unsymmetric trimmed modified Winsorized mean filter and decision based adaptive neighborhood median filter which takes the benefits of partial trimmed global mean, unsymmetric trimmed modified Winsorized mean and neighborhood pixel technique. The second phase is applied to wipe out the left over noisy pixels by replacing the processing pixel with the Winsorized mean of the first ordered neighborhood pixels. The proposed algorithm is examined upto 99% levels of salt and pepper noise for grey scale and color bitmap images and it gives better Peak Signal to Noise Ratio (PSNR), Image Enhancement Factor (IEF) and Structural Similarity Index (SSIM) values for high noise densities.

Enhanced Modified Decision-Based Unsymmetric Trimmed Adaptive Neighborhood Winsorized Mean Filter for Removing 1–99% Levels of Salt-and-Pepper Noise

In this paper, a novel and effective algorithm for removing 1–99% levels of salt-and-pepper noise is proposed. The proposed algorithm comprises a two-phase scheme. The first phase involves a combination of enhanced modified decision-based unsymmetric trimmed mean filter (EMDBUTMF) and decision-based unsymmetrical trimmed modified winsorized mean filter (DBUTWMF), and the second phase is employed after the first phase to replace the left over noisy pixels. The proposed algorithm is the fusion of the benefits of EMDBUTMF, DBUTWMF and decision-based diagonal neighborhood pixel algorithm and shows better results than the decision-based algorithm, modified decision-based unsymmetric trimmed median filter (MDBUTMF), EMDBUTMF, Modified decision-based unsymmetrical trimmed median filter with trimmed global mean (MDBUTMF_GM), DBUTWMF. The proposed algorithm is examined against 1–99% levels of salt-and-pepper noise for several grayscale bitmap images, and it gives better peak-signal-to-noise ratio, image enhancement factor and Structural Similarity Index Measures values for high noise densities.

Analysis of different digital filters for received signal strength indicator

Due to high demand in Internet of Things applications, researchers are exploring deeper alternative methods to provide efficiency in terms of application, energy, and cost among other factors. A frequently used technique is the Received Signal Strength Indicator value for different Internet of Things applications. It is imperative to investigate the digital signal filter for the Received Signal Strength Indicator readings to interpret it into more reliable data. A contrasting analysis of three different types of digital filters is presented in this paper, namely: Simple Moving Average filter, Alpha Trimmed Mean filter and Kalman filter. There are three criteria used to observe the performance of these digital filters which are noise reduction, data proximity and delays. Based on the criteria, the choice of digital signal processing filter can be determined in accordance with its implementations in [ractice. For example, Alpha-Trimmed Meanfilter is shown to be more efficient if used in the pre-processing of Received Signal Strength Indicator readings for physical intrusion detection due to its high data proximity. Hence, this paper illustrates the possibilities of the use of Received Signal Strength Indicator in different Internet of Things applications given a proper choice of digital signal processing filter.

Image Inpainting Using Nonlocal Texture Matching and Nonlinear Filtering.

Nonlocal texture similarity and local intensity smoothness are both essential for solving most image inpainting problems. In this paper, we propose a novel image inpainting algorithm that is capable of reproducing the underlying textural details using a nonlocal texture measure and also smoothing pixel intensity seamlessly in order to achieve natural-looking inpainted images. For matching texture, we propose a Gaussian-weighted nonlocal texture similarity measure to obtain multiple candidate patches for each target patch. To compute the pixel intensity, we apply the -trimmed mean filter to the candidate patches to inpaint the target patch pixel-by-pixel. The proposed algorithm is compared with four current image inpainting algorithms under different scenarios, including object removal, texture synthesis, and error concealment. Experimental results show that the proposed algorithm outperforms the existing algorithms when inpainting large missing regions in images with texture and geometric structures.

Impulse noise detection technique based on fuzzy set

In this study, a new fuzzy-based technique is introduced for denoising images corrupted by impulse noise. The proposed method is based on the intuitionistic fuzzy set (IFS), in which the degree of hesitation plays an important role. The degree of hesitation of the pixels is obtained from the values of memberships of the object and the background of the image. After minimising the obtained hesitation function, the IFS is constructed and the noisy pixels are detected outside the neighbourhood of mean intensity of the object and the background of an image. Denoised images are relatively analysed with five other methods: modified decision-based unsymmetric trimmed median filter, noise adaptive fuzzy switched median filter, adaptive fuzzy switching weighted average filter, adaptive weighted mean filter, iterative alpha trimmed mean filter. Performances of the proposed method along with these five state-of the-art methods are evaluated using a peak signal-to-noise ratio and error rate along with the time for computation. Experimentally, derived denoising method showed an improved performance than five other existing techniques in filtering noise in images due to the reduction of uncertainty while choosing the noisy pixels.

High Density Salt and Pepper Noise Removal in Images Using Adapted Decision Based Unsymmetrical Trimmed Mean Filter Cascaded with Gaussian filter

An adapted decision based unsymmetrical trimmed mean filter cascaded with Gaussian filter (ADCG) algorithm for the retrieval of gray scaled image which is induced by a very high density Salt and Pepper (impulse) noise is proposed and tested in this paper. The proposed calculation supplants the noisy pixel by trimmed mean quality when other pixel values, 0's and 255's are available in the selected window and when all the pixel qualities are 0's and 255's then the noisy pixel is supplanted by mean estimation of the considerable number of components present in the selected window. This proposed methodology shows preferred results over the Standard Median Filter (MF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), Progressive Switched Median Filter (PSMF) and Modified Decision Based Unsymmetrical Trimmed Median Filter (MDBUTMF). The proposed methodology is tried against distinctive pictures and it gives better PSNR that is Peak Signal-to-Noise Ratio.

Efficient digital image denoising for gray scale images

We propose an algorithm for image de-noising which uses a trimmed global mean filter with rank order absolute differences to remove random-valued impulse noise, stripes, scratches and blotches. It is a two stage algorithm, in the first stage the corrupted candidate is detected using rank ordered absolute differences (ROAD). In the second stage, the corrupted pixels are replaced by the median of the uncorrupted pixels in the selected window. Trimmed global mean filter is used if the selected window contains all the pixels as noisy candidate. We used a fixed window size in both detection and filtering stages. The visual and quantitative results show that proposed filter outperforms the existing filters in restoring image which is corrupted by random valued impulse noise. The proposed algorithm also provides good results over the impulse noise image corrupted by artificially introduced scratches, blotches, and stripes without causing blurring.

Dedicated breast computed tomography: Volume image denoising via a partial‐diffusion equation based technique

Dedicated breast computed tomography (CT) imaging possesses the potential for improved lesion detection over conventional mammograms, especially for women with dense breasts. The breast CT images are acquired with a glandular dose comparable to that of standard two-view mammography for a single breast. Due to dose constraints, the reconstructed volume has a non-negligible quantum noise when thin section CT slices are visualized. It is thus desirable to reduce noise in the reconstructed breast volume without loss of spatial resolution. In this study, partial diffusion equation (PDE) based denoising techniques specifically for breast CT were applied at different steps along the reconstruction process and it was found that denoising performed better when applied to the projection data rather than reconstructed data. Simulation results from the contrast detail phantom show that the PDE technique outperforms Wiener denoising as well as adaptive trimmed mean filter. The PDE technique increases its performance advantage relative to Wiener techniques when the photon fluence is reduced. With the PDE technique, the sensitivity for lesion detection using the contrast detail phantom drops by less than 7% when the dose is cut down to 40% of the two-view mammography. For subjective evaluation, the PDE technique was applied to two human subject breast data sets acquired on a prototype breast CT system. The denoised images had appealing visual characteristics with much lower noise levels and improved tissue textures while maintaining sharpness of the original reconstructed volume.

Modified repeated median filters

We discuss moving window techniques for fast extraction of a signal composed of monotonic trends and abrupt shifts from a noisy time series with irrelevant spikes. Running medians remove spikes and preserve shifts, but they deteriorate in trend periods. Modified trimmed mean filters use a robust scale estimate such as the median absolute deviation about the median (MAD) to select an adaptive amount of trimming. Application of robust regression, particularly of the repeated median, has been suggested for improving upon the median in trend periods. We combine these ideas and construct modified filters based on the repeated median offering better shift preservation. All these filters are compared w.r.t. fundamental analytical properties and in basic data situations. An algorithm for the update of the MAD running in time O(log n) for window width n is presented as well.

Sorting continuous-time signals: analog median and median-type filters

There are two natural orderings in signals: temporal order and rank order. There is no compelling reason to explore only one of these orderings, either in the discrete-time or in the continuous-time case. Nevertheless, the concept of rank order for continuous-time signals remains virtually neglected, which is in striking contrast to the discrete-time case: ranked order discrete-time filters, of which the running median is the most common example, have been intensively studied for three decades. The dependence of these nonlinear systems on the order statistics of the input samples stands in contrast with the tapped delay line filter, which depends on temporal order only. However, continuous-time signals can also be meaningfully sorted: a fact that is explored in this paper to define and study the analog median filter and other ranked-order filters. The paper introduces the basic tools needed to analyze and understand these continuous-time nonlinear filters (the distribution function and the sorting) and presents some of their properties in a tutorial way. The analog median filter is defined in terms of the (unique) nonincreasing left-continuous sorting. More general filters can also be defined, including filters similar to /spl alpha/-trimmed mean filters and L filters. These include filters that depend on one parameter and contain the running average and running median as special cases. The rate of convergence of the digital median filter to the analog median filter is discussed and related to the signal sampling period, the duration of the filter window, and the smoothness of the input signal. The paper introduces the concept of noise width and studies the effect of additive and multiplicative noise at the output of the analog median filter in terms of the noise width and the smoothness of the input signal.

An adaptive snake model for ultrasound image segmentation: modified trimmed mean filter, ramp integration and adaptive weighting parameters.

The snake model is a widely-used approach to finding the boundary of the object of interest in an ultrasound image. However, due to the speckles, the weak edges and the tissue-related textures in an ultrasound image, conventional snake models usually cannot obtain the desired boundary satisfactorily. In this paper, we propose a new adaptive snake model for ultrasound image segmentation. The proposed snake model is composed of three major techniques, namely, the modified trimmed mean (MTM) filtering, ramp integration and adaptive weighting parameters. With the advantages of the mean and median filters, the MTM filter is employed to alleviate the speckle interference in the segmentation process. The weak edge enhancement by ramp integration attempts to capture the slowly varying edges, which are hard to capture by conventional snake models. The adaptive weighting parameter allows weighting of each energy term to change adaptively during the deformation process. The proposed snake model has been verified on the phantom and clinical ultrasound images. The experimental results showed that the proposed snake model achieves a reasonable performance with an initial contour placed 10 to 20 pixels away from the desired boundary. The mean minimal distances from the derived boundary to the desired boundary have been shown to be less than 3.5 (for CNR > or = 0.5) and 2.5 pixels, respectively, for the phantom and ultrasound images.

Structure-adaptive image filtering using order statistics

In this article a structure-adaptive approach to the evaluation of image local properties for adaptive filtering is described. The adaptive procedure is based on selection of the most homogeneous neighborhood region from several possible structuring regions by the principle of maximum posterior probability. Then, an optimal evaluation of the pixel value at this point is performed involving pixels from the determined neighborhood region and the symmetric structuring region. The trimmed mean filters are used for the robust evaluation of local properties during estimation of object and background intensities when the supposed additive noise has a mixed conditional distribution, e.g., normal distribution with outliers. A time-efficient scheme for fast implementation of this method is proposed as well.

Postprocessing of transform coded images via histogram-based edge classification

Quantization noise prevalent in transform encoded images becomes increasingly objectionable as the required bit rate for the compressed image representation is reduced. The perceptual effect of this coding noise is not uniform throughout the image, however, being highly dependent on the local behavior of the signal on which it is superimposed. An adaptive, nonlinear postprocessing algorithm is described, which is shown to appreciably enhance the subjective quality of the reconstructed image. A three-component image model is adopted, according to which any image is considered to be composed of nonoverlapping strong edge, textured, and monotone components. The foundation of the postprocessing algorithm is a computationally efficient edge classifier capable of resolving an image into its three components. The classifier operates by exploiting the characteristic shape of the histogram of pixel luminance values in a strong edge region to distinguish between strong and textured edges. The postprocessing algorithm consists of a combination of adaptive, a trimmed mean filtering (where the a value and window size are determined by the output of the edge classifier) and a cosine transform domain dithering technique. The results presented confirm the efficacy of the proposed approach.

Conditional trimmed mean filters and their applications for noise removal

In this paper, a conditional trimmed mean (CTM) filter and its modification, called enhanced conditional trimmed mean (ECTM) filter are proposed. Their implications do not require to sort the windowed data and the influence of an incorrect reference point on the output of the filter is reduced by using a conditional test. The extension of CTM filters to the two dimensional domain is also developed. Simulations show that the proposed filters perform better in noise removal as well as in fine structure preservation.

Median-style filters for noise reduction in composition analyses

Several commonly applied techniques for atom probe data noise reduction are compared to signal processing techniques specifically designed to maintain interfacial integrity. A median filter and a double-window modified trimmed mean filter, along with several other filters, are applied to both a simulated atom probe composition profile and to real atom probe data. The various methods are compared using average profile compositions and variance measurements. The double-window modified trimmed mean filter is found to smooth the data in the slowly varying regions of the profile without degrading sharp interfaces.

Multivariate ordering in color image filtering

Multivariate data ordering and its use in color image filtering are presented. Several of the filters presented are extensions of the single-channel filters based on order statistics. The statistical analysis of the marginal order statistics is presented for the p-dimensional case. A family of multichannel filters based on multivariate data ordering, such as the marginal median, the vector median, the marginal alpha -trimmed mean, and the multichannel modified trimmed mean filter, is described in detail. The performance of the marginal median and the vector median filters in impulsive noise filtering is investigated. Simulation examples of the filters under study are described. >

A comparison of three different filters for speckle reduction of doppler spectra

This paper is concerned with speckle suppression in Discrete Fourier Transform based Doppler signals by means of digital image processing. The Doppler spectrum is treated as a greyscale image, and three different noise smoothing algorithms are applied to it. These are the Double Window Modified Trimmed Mean filter, which is nonlinear, and Lee's and the directional filter, which are adaptive in the sense that the smoothing performed by them at each point of the image is controlled by a local image measure. In order to evaluate the performance of the filters, they were applied to a variety of regular waveforms obtained from a physiological flow phantom. Ensemble averaging of a large number of unfiltered spectra was used as the “gold standard” in the evaluation, i.e., as the output of an ideal filter which reveals the exact nature of the underlying Doppler spectrum after speckle has been eliminated. Comparison of the “gold standard” with the ensemble averaged filtered data allowed the noise reduction, bias and distortion of the maximum frequency envelope introduced by filtering to be examined. Overall, the best performance was offered by the directional filter whose action was controlled by the combination of the local edge content and the slope of the least-squares-fit line passing through the data points along each particular direction.