Alpha-trimmed Mean Filter Research Articles

Efficient Classification of Marine Debris using SVM with Noise Removal and Feature Extraction Techniques with Improved Performances

Marine debris is a significant environmental issue, necessitating the development of precise and efficient technologies for classifying and reducing its impact. This research tries to address the issues of image noise in ocean trash classification using four alternative filters the Gaussian, Bilateral, Mean, and Alpha-Trimmed Mean (ATM) filters for noise reduction and SVM for classification. The accuracy of categorization algorithms can be considerably impacted by the presence of noise in photos of maritime debris. In this study, we suggest a two-pronged strategy: To effectively decrease noise and improve the quality of the input photos, we first apply filters. These filters were chosen with care to protect significant details while eliminating extraneous noise. Second, we classify the debris into various categories based on its visual attributes using SVM, a powerful ML technique. The ATM filter significantly reduces noise and improves the clarity of photographs of ocean trash, according to the results of our experiments. This work proposes a novel approach for classifying marine debris using advanced machine learning algorithms. For improved classification accuracy, we suggest combining Support Vector Machines (SVM) with Adaptive Thresholding Mean (ATM) filtering and Histogram of Oriented Gradients (HOG) feature extraction. According to this research, the ATM filter is a promising option for noise reduction in ocean trash imaging, potentially increasing the precision of subsequent classification algorithms and assisting in efficient environmental monitoring and marine ecosystem conservation efforts. The language used for execution is Python.

Simultaneous source separation and noise reduction with structure-oriented space-varying alpha-trimmed mean filter

Median filters are often used in simultaneous source data to remove sudden noise disturbances. Nonetheless, the median filter has a poor ability to process random noise, which can affect its performance in seismic data processing. This paper introduces a novel filtering method, a structure-oriented space-varying alpha-trimmed mean filter, which is an improvement of median filtering, and can deal with random noise better. The proposed filter's window length can be adaptively adjusted based on geological conditions, enabling the effective removal of spiky noise during simultaneous source acquisition and common random noise in geological exploration. This paper introduces the main differences between different types of median filtering methods and provides an overview of the principles and steps involved in our proposed approach. Our method's effectiveness is evaluated through its application to both simple synthetic examples and field data, alongside a comparative analysis against other existing approaches. The results of this research demonstrate that the approach we proposed here can efficiently make spike-like noise and random noise disappear while simultaneously maintaining vital signal characteristics.

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.

Lung Cancer Segmentation and Classification Using Integration of Convolutional Neural Network & Unet Network Over CT Images: A Deep Learning Approach

Purpose: Cellular breakdown in the lungs screening is a cycle that is utilized to recognize the presence of cancer in the lungs in any case. Mostly it occurs among the elderly, especially smokers. Lung infections are lung-affecting illnesses that impede the respiratory mechanism. Cellulose breakdown in the lungs is one of the top causes of mortality in people all over the globe. Early recognition can improve endurance chances. As the world is revolutionizing with so many emerging technologies and one of the most popular technology is Deep Learning (DL) which has shown tremendous development in medical fields. So this paper brings an effective deep learning framework for lung cancer detection. Objective: To develop a CAD system for efficient lung cancer detection from CT images using a combination of CNN and UNET. For improving accuracy in the proposed system a better feature extraction and feature selection techniques is incorporate, i.e., by using autoencoder and selection based on Kernal function for effective boostings. Methodology: For this deep learning framework, the following are the stages. (a) Data Collection from the popular repository IQ-OTHNCCD lung cancer dataset which contains CT images of a total of 1198 from 110 CT slice cases, (b) Preprocessing CT images with an alpha-trimmed mean filter and CLAHE for improved enhancement, (c) Segmentation using CNN for segmenting the cancer regions) with the use of an autoencoder, extracting characteristics like area, perimeter, centroid, and mean intensity) feature selection using kernel function and finally f) classification using UNet network. Findings/Result: In terms of accuracy, sensitivity, specificity, recall, precision, F1-score, detection rate, TPR, FPR, and computation time, experiments are carried out on a range of cutting-edge models, and our suggested model surpasses them all (accuracy:0.95, sensitivity:0.97, specificity:0.98, detection rate:0.94). Originality: This paper is incorporating 2 neural networks over main stages such as segmentation and classification which eventually improves the quality of the model higher and also these are performed over real-time public medical records which shows the novelty of the model. Paper type: Methodology paper

Design an efficient disease monitoring system for paddy leaves based on big data mining

With the progressions in Information and Communication Technology (ICT), the innumerable electronic devices (like smart sensors) and several software applications can proffer notable contributions to the challenges that are existent in monitoring plants. In the prevailing work, the segmentation accuracy and classification accuracy of the Disease Monitoring System (DMS), is low. So, the system doesn't properly monitor the plant diseases. To overcome such drawbacks, this paper proposed an efficient monitoring system for paddy leaves based on big data mining. The proposed model comprises 5 phases: 1) Image acquisition, 2) segmentation, 3) Feature extraction, 4) Feature Selection along with 5) Classification Validation. Primarily, consider the paddy leaf image which is taken as of the dataset as the input. Then, execute image acquisition phase where 3 steps like, i) transmute RGB image to grey scale image, ii) Normalization for high intensity, and iii) preprocessing utilizing Alpha-trimmed mean filter (ATMF) through which the noises are eradicated and its nature is the hybrid of the mean as well as median filters, are performed. Next, segment the resulting image using Fuzzy C-Means (i.e. FCM) Clustering Algorithm. FCM segments the diseased portion in the paddy leaves. In the next phase, features are extorted, and then the resulted features are chosen by utilizing Multi-Verse Optimization (MVO) algorithm. After completing feature selection, the chosen features are classified utilizing ANFIS (Adaptive Neuro-Fuzzy Inference System). Experiential results contrasted with the former SVM classifier (Support Vector Machine) and the prevailing methods in respect of precision, recall, F-measure,sensitivity accuracy, and specificity. In accuracy level, the proposed one has 97.28% but the prevailing techniques only offer 91.2% for SVM classifier, 85.3% for KNN and 88.78% for ANN. Hence, this proposed DMS has more accurate detection and classification process than the other methods. The proposed DMS evinces better accuracy when contrasting with the prevailing methods.

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.

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.

Smartphone-based heart-rate measurement using facial images and a spatiotemporal alpha-trimmed mean filter.

Currently, cardiovascular disease affects a relatively high proportion of the world's population. Thus, developing simple and effective methods for monitoring patients with cardiovascular disease is critical for research. Monitoring the heart rate of patients is a relatively simple and effective method for managing patients with this condition. For patients, the desired heart rate monitoring equipment should be portable, instantaneous, and accurate. Because smartphones have become the most prevalent mobile device, we utilized this technology as a platform for developing a novel heart-rate measurement system. Catering to the phenomenon of people using the front camera of their smartphones as a mirror, the proposed system was designed to analyze facial-image sequences captured using the front camera. A spatiotemporal alpha-trimmed mean filter was developed to estimate a user's heart rate quickly and accurately. The experimental results show that in addition to achieving these objectives, the developed system outperforms a similar personal computer-based system. In addition, the system performs effectively even when users are wearing glasses. Hence, the proposed system demonstrates practical value for people who must monitor their heart rate daily.

A License Plate Image Enhancement Method in Low Illumination Using BEMD

Recognizing vehicle license plate image captured in low illumination place is a difficult problem. To solve the problem, this paper proposes a new license plate image enhancement method using bidimensional empirical mode decomposition (BEMD) technique. BEMD is a 2D data-driven adaptive nonlinear signal decomposition approach derived from the 1D empirical mode decomposition (EMD). In the proposed algorithm, the main procedures are designed as the following: first, the license plate image is denoised by the use of alpha-trimmed mean filter and transformed from RGB color space into HSV color space, then, extract the V component to form intensity image for enhancement; second, with BEMD method, the intensity image is decomposed into a number of intrinsic mode functions (IMF) as well as a residual image; last, the brightness of residual image is adjusted using Retinex theory, and fused with the IMF images to achieve enhancement of license plate image. Experimental results show the proposed method provides superior performance over traditional schemes for license plate image enhancement in low illumination.

An Adaptive Filtering Algorithm Based on Improved Gray Relational Analysis

Because of the characteristic of the gray relation analysis and the advantage of the alpha-trimmed mean filter, an efficient technique for mixed noise removal in images was proposed. This algorithm can adjust the filter coefficients adaptively according to various pieces of the image features. Experiment results show that the proposed algorithm which greatly improved efficiently, it not only can remove mixed noise in image, but also can keep the details of the image.

Speckle reduction of ultrasound images based on Rayleigh-trimmed anisotropic diffusion filter

A novel method is proposed to reduce speckle in ultrasound images. Based on the assumption of Rayleigh distribution of speckle, a Rayleigh-trimmed filter is first proposed to estimate the relative standard deviations of local signals and the results are used to determine the parameter that controls an alpha-trimmed mean filter for suppressing the primary noise. Then the anisotropic diffusion is subsequently applied to further reduce noise while enhancing features and structures in the original image. We also extend the proposed method to three-dimensional space by introducing time as one additional dimension. The proposed method effectively utilizes the statistical characteristics of speckle and the two-step despeckling algorithm reduces speckle significantly while retaining important features. The effectiveness of the proposed method is well demonstrated by experiments on both simulated and real ultrasound images.

天文观测CCD相机中Smear效应的消除

On the basis of the mechanism of Smear effect and the image characteristics of a frame transfer CCD camera,a Smear removal algorithm which is based on the high precision background estimation is proposed.It can be applied to not only a single picture,but also sequences for imaging systems.This method firstly uses a statistic method to analyze the gray distribution of an original image to get the specific location of Smear effect automatically,then it takes Alpha-trimmed Mean Filter(ATMF) to estimate the intensity of the Smear effect exactly.Finally the difference way is used to eliminate the Smear effect.Experimental results show that this method can effectively eliminate the single or multi-Smear effects,and can ultimately improve the image quality.The proposed method meets the requirements of astronomic observing cameras for Smear removal.

FMCW 방식의 전파 고도계에서 수신 신호 요동에 대한 영향을 감소하기 위한 다중 필터의 설계

본 논문은 FMCW 방식 전파 고도계에서 발생되는 수신신호 요동을 감소시키고, 스텝으로 변화되는 고도의 모서리 검출 특성을 향상시키기 위한 다중필터를 제안하였다. 제안된 필터는 Alpha-trimmed 평균필터와 주파수 변화 제한기, 그리고 1/3 order static필터로 구성되었다. 수신 신호 분석을 이용한 시뮬레이션은 제안된 필터가 이동 평균 필터 및 메디안 필터와 비교하여 오차 감소 성능이 우수함을 확인하였다. 특히, 지면 정지 비행에서 신호의 요동을 제거시키는 능력이 크게 향상되었다. This paper proposes a multiple filter for reduction of received signal fluctuation and enhancement of step altitude edge detection in FMCW radar altimeter. The proposed filter was composed of alpha-trimmed mean filter, frequency variation limiter, and 1/3 order static filter. Simulations by analysis of received signal show that the proposed filter provides better performance than moving average and standard median filters with error reduction. In particular, the proposed filter was improved in the ability of reducing fluctuation for ground hovering.

An adaptive filter based on images' entropy

An adaptive filter to remove mixed Gaussian noise and impulse noise of the corrupted images based on their minimum entropies was proposed. The proposed filter calculated the noise rate of the corrupted image based on the minimum local entropy, and utilized the noise rate to determine the size of filter windows and the number of pixels to be brushed off in the filter windows of Alpha-trimmed mean filter, and then used the average of leaved pixels' gray as the output of the filter. The performance of the proposed filter was evaluated by experiments. Experimental results show that the superiority of this filter including the ability of removing noises and that of preserving the partial details of images in comparison with some existing methods (median filter and mean filter), and this filter is an adaptive Alpha-trimmed mean filter.

Extractive Support Vector Algorithm on Support Vector Machines for Image Restoration

The major problem of SVMs is the dependence of the nonlinear separating surface on the entire dataset which creates unwieldy storage problems. This paper proposes a novel design algorithm, called the extractive support vector algorithm, which provides improved learning speed and a vastly improved performance. Instead of learning and training with all input patterns, the proposed algorithm selects support vectors from the input patterns and uses these support vectors as the training patterns. Experimental results reveal that our proposed algorithmprovides near optimal solutions and outperforms the existing design algorithms. In addition, a significant framework which is based on extractive support vector algorithm is proposed for image restoration. In the framework, input patterns are classified by three filters: weighted order statistics filter, alpha-trimmed mean filter and identity filter. Our proposed filter can achieve three objectives: noise attenuation, chromaticity retention, and preservation of edges and details. Extensive simulation results illustrate that our proposed filter not only achieves these three objectives but also possesses robust and adaptive capabilities, and outperforms other proposed filtering techniques.

Image filtering for improved dose resolution in CT polymer gel dosimetry

X-ray computed tomography (CT) has been established as a feasible method of performing dosimetry using polyacrylamide gels (PAGs). A small density change occurs in PAG upon irradiation that provides contrast in PAG CT images. However, low dose resolution limits the clinical usefulness of the technique. This work investigates the potential of using image filtering techniques on PAG CT images in order to reduce image noise and improve dose resolution. CT image noise for the scanner and protocol used for the gel images is analyzed and found to be Gaussian distributed and independent of the contrast level in the images. As a result, several filters for reducing spatially invariant noise are investigated: mean, median, midpoint, adaptive mean, alpha-trimmed mean, sigma mean, and a relatively new filter called SUSAN (smallest univalue segment assimilating nucleus). All filters are applied, using 3x3, 5x5, and 7x7 pixel masks, to a CT image of a PAG irradiated with a stereotactic radiosurgery dose distribution. The dose resolution within 95% confidence (D(delta)95%) is calculated and compared for each filtered image, as well the unfiltered image. In addition, the ability of the filters to maintain the spatial integrity of the dose distribution is evaluated and compared. Results clearly indicate that the filters are not equal in their ability to improve D(delta)95% or in their effect on the spatial integrity of the dose distribution. In general, increasing mask size improves D(delta)95% but simultaneously degrades spatial dose information. The mean filter provides the greatest improvement in D(delta)95%, but also the greatest loss of spatial dose information. The SUSAN, mean adaptive, and alpha-trimmed mean filters all provide comparable, but slightly poorer dose resolution. In addition, the SUSAN and adaptive filters both excel at maintaining the spatial distribution of dose and overall are the best performing filters for this application. The midpoint filter, normally useful for Gaussian noise, is poor all-round, dramatically distorting the dose distribution for masks greater than 3x3. The median filter, a common edge preserving noise reduction filter, performs moderately well, but artificially increases high dose gradients. The sigma filter preserves the spatial distribution of dose very well but is least effective at improving dose resolution. In summary, dose resolution can be significantly improved in CT PAG dosimetry through postprocessing of CT images using spatial noise reduction filters. However, such filters are not equal in their ability to improve dose resolution or to maintain the spatial integrity of the dose distribution and an appropriate filter must be chosen depending on clinical demands of the application.

Sampled-function weighted order filters

In this paper, a new type of L-filter, called a sampled-function weighted order (SFWO) filter is proposed. The coefficients of this filter are samples of a bounded real-valued function. This weighting function is derived for any given noise distribution by examining the asymptotic behavior of the corresponding L-filter coefficients. SFWO filters designed in this way constitute a good compromise between alpha-trimmed mean filters, which are easy to design, and optimal L-filters, which are more flexible but difficult to design. In fact, for a given class of distributions, a SFWO filter can be designed in the form of a smoothly-trimmed mean filter with one or more parameters and can perform as well as the optimal L-filter for that class. Design examples are given for Gaussian, Laplacian, Cauchy, triangular, parabolic, and uniform noise densities. Simulations show that SFWO filters are very promising for noise densities, the tail lengths of which vary from very short to very long.

Realization of an alpha-trimmed mean filter on the threshold decomposition architecture

The realization of the α-trimmed mean filter on the threshold decomposition architecture is described. The threshold decomposition architecture is developed for facilitating realization of nonlinear, order-statistic filters. In this architecture, an input sequence is thresholded at different levels to form a number of binary sequences. The filter output is formed by combining the decision results made at various levels; at each level it is determined whether the signal should be above the associated threshold level. The decision rule used is the distinguishing feature of an individual filter; the current implementation of the α-trimmed mean filter is based on using a threshold logic function as the decision rule, thus allowing the decision rule to output multiple values. The statistical properties and performance of the a-trimmed mean filter are analyzed on the basis of this realization.

A note on the use of nonlinear filtering in computer graphics

The application of nonlinear median and alpha-trimmed mean filters to computer graphics is discussed. The filters are simple to implement, do not require large amounts of CPU time to execute, and can be applied iteratively for better control over the filtering effects. A series of examples shows that the filters eliminate spike noise in a stochastic sampling application without smearing the final image.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Some statistical properties of alpha-trimmed mean and standard type M filters

The statistical properties of two classes of filters generalizing the median filter are considered. The two classes are the alpha-trimmed mean ( alpha -TM) filter and the standard type M(STM) filter, both of which are special cases of the L filter. Results are developed to quantify the white-noise suppression and edge-preservation characteristics of the filters by considering their output sequence error statistics. It is shown that the alpha -TM and STM filters perform better than the running mean and median filters in white noise suppression, while they can be designed to be comparable to the median filter in edge preservation in the presence of noise.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>