Mean Filter Research Articles

Validity of non-local mean filter and novel denoising method

BackgroundImage denoising is an important topic in the digital image processing field. This study theoretically investigates the validity of the classical nonlocal mean filter (NLM) for removing Gaussian noise from a novel statistical perspective. MethodBy considering the restored image as an estimator of the clear image from a statistical perspective, we gradually analyze the unbiasedness and effectiveness of the restored value obtained by the NLM filter. Subsequently, we propose an improved NLM algorithm called the clustering-based NLM filter that is derived from the conditions obtained through the theoretical analysis. The proposed filter attempts to restore an ideal value using the approximately constant intensities obtained by the image clustering process. In this study, we adopt a mixed probability model on a prefiltered image to generate an estimator of the ideal clustered components. ResultThe experiment yields improved peak signal-to-noise ratio values and visual results upon the removal of Gaussian noise. ConclusionHowever, the considerable practical performance of our filter demonstrates that our method is theoretically acceptable as it can effectively estimate ideal images.

Evaluating denoising performances of basic filters in the detection of microcalcifications on mammogram images

This research evaluates the denoising abilities of some image-processing filters used in facilitating the early detection of microcalcifications in breast tissues. The mean, median and Gaussian filters were employed to denoise mammogram images of microcalcification breast phantoms of various densities. The performances of the filters were assessed by evaluating the mean squared error (MSE), peak signal-to-noise ratio (PSNR), and signal-to-noise ratio (SNR). All experiments were carried out on MATLAB R2020a platform. The results revealed that the Gaussian filter recorded optimal performance in denoising images with all 3 types of added noises compared to the mean and median filters. The PSNR value of the heterogeneous phantom (PVAL/H) was superior to those of the less dense (PVAL/E), dense (PVAL), and extremely dense (PVAL/G) phantoms for all the tested filters. The results of this work agree with the high contrast recorded by the original image of PVAL/H.

Canonical mean filter for almost zero-shot multi-task classification

Canonical mean filter for almost zero-shot multi-task classification

Towards a Lightweight Classifier to Detect Hypovolemic Shock.

Predicting the ability of an individual to compensate for blood loss during hemorrhage and detect the likely onset of hypovolemic shock is necessary to permit early clinical intervention. Towards this end, the compensatory reserve metric (CRM) has been demonstrated to directly correlate with an individual's ability to maintain compensatory mechanisms during loss of blood volume from onset (one-hundred percent health) to exsanguination (zero percent health). This effort describes a lightweight, three-class predictor (good, fair, poor) of an individual's compensatory reserve using a linear support-vector machine (SVM) classifier. A moving mean filter of the predictions demonstrates a feasible model for implementation of real-time hypovolemia monitoring on a wearable device, requiring only 408 bytes to store the models' coefficients and minimal processor cycles to complete the computations.

Research on Crop 3D Model Reconstruction Based on RGB-D Binocular Vision

Taking maize seedlings as the object, the implementation of crops 3D reconstruction based on RGB-D binocular vision and the selection of some key parameters are investigated in this research. First, multiple images are taken from different angles around the target. By mapping the maize seedling region coordinate values after the Otsu algorithm and global threshold segmentation to the corresponding depth image, the depth data of the maize seedling region can be obtained accurately. An improved mean filter is proposed to adaptively fill the holes in the depth image. Then, the different point clouds with the fixed step angle of the maize seedling are registered and fuzed. Finally, after the fusion point cloud is simplified, the 3D model of crops can be reconstructed. Experimental results show that the simplification effect of the octree algorithm is better than that of the voxel grid filter. Among all the step angles, the reconstruction error of the step angle with 60° is the smallest. Under this condition, the height error between the model and the maize seedling is 2.22%, and the error in stem diameter is 11.67%.

Comparative Analysis on De-Noising of MRI Uterus Image for Identification of Endometrial Carcinoma

Purpose: The anatomical and physiological processes of the human body are pictured in radiology using different modalities. Magnetic Resonance Imaging (MRI) supports capturing the images of organs using magnetic field gradients. The quality of MR images is generally affected by various noises such as Gaussian, speckle, salt and pepper, Rayleigh, Rican etc. Removal of these noises from the MR images is essential for further diagnostic procedures.
 Materials and Methods: In this article, Gaussian noise, speckle noise, and salt and pepper noise are added to the MR uterus image for which different filters are applied to remove the noise for precise identification of endometrial carcinoma.
 Results: The different filters incorporated for the additive noise removal process are the bilateral filter, Non-Local Means (NLM) filter, anisotropic diffusion filter, and Convolution Neural Network (CNN). The efficiency of the filter is calculated by evaluating the response of the filter by gradually increasing the noise intensity of the MR images.
 Conclusion: Further, peak Signal-to-Noise Ratio (SNR), structural similarity index measure, image quality index and computational cost parameters are computed and analyzed.

Vectorization Programming Based on HR DSP Using SIMD

Single instruction multiple data (SIMD) vector extension has become an essential feature of high-performance processors. Architectures such as x86, ARM, MIPS, and PowerPC have specific vector extension instruction sets and SIMD micro-architectures. Using SIMD vectorization programming can significantly improve the performance of application algorithms while keeping the hardware overhead low. In addition, other methods can enhance algorithm performance, such as selecting the best SIMD vectorization model for algorithms, ensuring sufficient instruction streams, implementing reasonable and effective cache data prefetching, and aligning data access and storage addresses according to instruction characteristics. The goal of this paper is three-fold. First, we introduce the basic structural characteristics of a general RISC processor, Hua Rui (HR) DSP, with a custom vector instruction set based on compatibility with an MIPS64 fixed-point and floating-point instruction set, as well as a Fei Teng (FT) processor compatible with an ARMv8 instruction set. Second, we summarize the fundamental principles of SIMD vectorization programming design for the HR DSP, which provides ideas for other scholars or engineering and technical personnel to study the algorithm performance using SIMD vectorization optimization. Third, we implement representative typical algorithms based on the HR and FT platforms and obtain experimental results that show improvement in algorithm SIMD vectorization optimization according to the vector programming design principles summarized in this article can improve the single-core performance of scalar implementation without vectorization, instruction streams, and cache data prefetching by 4–22 times for mean filter, accumulation, and matrix–matrix multiplication, which is significantly better than the performance improvement of 3–13 times for the FT platform. Moreover, the performance of matrix–matrix multiplication using the best vectorization model on the HR platform is about 84% higher than that of the common SIMD vectorization model.

Connected pixels-based image smoothing filter

Digital image processing heavily relies on the connectivity of pixels, as it is a vital component for accurate object identification and analysis within an image. Grouping together pixels with similar features such as colour and intensity, allows for the formation of meaningful patterns or objects, which is essential for object recognition and segmentation. This approach is particularly valuable in photogrammetric imaging, video surveillance, deep learning as it facilitates the isolation of regions of interest and object tracking. Image smoothing is also a crucial aspect in enhancing visual quality by reducing noise and enhancing details, especially in applications such as aerial mapping, medical imaging, video compression, image resizing and computer vision. The absence of connected pixels and image smoothing would make image processing tasks more challenging and less reliable, making them fundamental to digital image processing and critical to various applications in diverse fields. This paper introduces a novel image smoothing filter called Connected Pixels Based Image Smoothing Filter (CPF), which is based on gray connected pixels. The success of the CPF was compared to that of the Non-Local Means Filter (NLMF) in terms of Structural Similarity Index (SSIM) for the same Mean Squared Error (MSE). The experimental results showed that CPF has a better ability to preserve image details compared to NLMF.

Low cost inertial sensor attitude fixation algorithm and accuracy analysis

Aiming at the problems of angle oscillation, slow convergence speed, and low accuracy of traditional algorithms in low cost inertial element attitude determination, this study proposes an improved attitude algorithm based on geomagnetic correction robust model assisted adaptive step gradient descent. Firstly, a robust ellipsoid fitting algorithm is proposed to correct the geomagnetic anomaly data. Then, an adaptive step size model is established based on the momentum gradient descent method, and the iterative optimization parameters are automatically adjusted according to the error function. At the same time, the dynamic limiting mean filter is used to constrain the attitude angle, to calculate the optimal attitude information of the sensor. Finally, the algorithm is verified by static and dynamic experimental data. The results show that the improved algorithm can effectively suppress the angle oscillation, optimize the attitude convergence effect, and improve the accuracy of low cost inertial sensor attitude calculation.

Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals

Detecting human head movement during sleep is important as it can help doctors to assess many physical or mental health problems, such as infantile eczema, calcium deficiency, insomnia, anxiety disorder, and even Parkinson’s disease, and provide useful clues for accurate diagnosis. To obtain the information of head movement during sleep, current solutions either use a camera or require the user to wear intrusive sensors to collect the image or motion data. However, the vision-based schemes rely on light conditions and raise privacy concerns. Many people, including the elderly and infants, may be reluctant to wear wearable devices during sleep. In this paper, we propose Wi-Senser, a nonintrusive and contactless smart monitoring system for detecting head movement during sleep. Wi-Senser directly reuses the existing WiFi infrastructure and exploits the fine-grained channel state information (CSI) of WiFi signals to capture the minute human head movement during sleep without attaching any sensors to the human body. Specifically, we constructed a filtering channel including a Hampel filter, wavelet filter, and mean filter to remove outliers and noises. We propose a new metric of carrier sensitivity to select an optimal subcarrier for recording the change in targeted body movement from 30 candidate subcarriers. Finally, we designed a peak-finding algorithm to capture the real peak set recording the change in human head movement. We designed and implemented Wi-Senser with just one commercial off-the-shelf (COTS) router and one laptop equipped with an Intel 5300 network interface card (NIC). We evaluated the performance of Wi-Senser with 10 volunteers (6 adults and 4 children). Extensive experiments demonstrate that Wi-Senser can achieve 97.95% accuracy for monitoring head movement during sleep. Wi-Senser provides a new solution for achieving noninvasive, continuous, and accurate detection of minute human movement without any additional cost.

Image denoising using adaptive bi-dimensional stochastic resonance system

Using stochastic resonance (SR) mechanism, the output signal can be enhanced by adding noise to the nonlinear system. Therefore, an image denoising algorithm based on adaptive bi-dimensional stochastic resonance (ABSR) is proposed in this paper. Firstly, the image is sampled as a bi-dimensional signal, and an adaptive bi-dimensional dynamic nonlinear system model is constructed. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the output image are used as the double evaluation model of the adaptive system, and the optimal parameters of the model are automatically obtained by adjusting the parameters of the dynamic nonlinear system using the reverse positioning method. Compared with the traditional mean filter, median filter and one-dimensional stochastic resonance, the image restoration effect of dynamic adaptive bi-dimensional stochastic resonance is more closer to the original image, and the histogram, PSNR and SSIM of the output image are also significantly better than the other three methods. The results show that dynamic adaptive bi-dimensional stochastic resonance has better denoising effect and better robustness to the change of noise intensity in image processing.

Abnormal event detection model using an improved ResNet101 in context aware surveillance system

AbstractSurveillance system plays a significant role for achieving security monitoring in the place of crowd areas. Offline monitoring of these crowd activity is quite challenging because it requires huge number of human resources for attaining efficient tracking. For shortcoming these issue automated and intelligent based system must be developed for efficiently monitor crowd and detect abnormal activity. However the existing methods faces issues like irrelevant features, high cost and process complexity. In this current research context aware surveillance‐system utilising hybrid ResNet101‐ANN is developed for effective abnormal activity detection. For this proposed approach video acquired from surveillance camera is considered as input. Then, acquired video is segmented into multiple frames. After that pre‐processing techniques such as denoising using mean filter, motion deblurring, contrast enhancement using Histogram Equalisation and canny edge detection is applied in this segmented frames. Further, the pre‐processed frame is fetched into hybrid ResNet101‐ANN classifier for abnormal event classification. Here, ResNet101 is used for extracting the features from the frames and Artificial neural network which replaces the fully connected layer of ResNet101 us used to detect the abnormal activity. If once abnormal‐events detected the context aware services generate alert to the user for preventing abnormal‐activities. Accuracy, precision, recall, and error values reached for the proposed‐model on simulation were 0.98, 0.98, 0.98 and 0.017 respectively. Using this proposed model effective crowd monitoring and abnormal activity detection can be achieved.

Comparison of processing results of median filter and mean filter on Gaussian noise

In people's daily lives, if the image is polluted with noise, it will become very blurred, so it is pretty necessary to use filtering to denoise the image and get a clearer image to assist people in work and study. The purpose of this article is by comparing the denoising result of the Median filter and Mean filter on Gaussian noise, and the filtering method which is more suitable for reducing Gaussian noise is found. Matlab is a very accurate and reliable scientific calculation standard software tool, which is very common in people's lives. The Median filter is a kind of nonlinear filter. It is so virtual at decreasing impulse noise. The common basic theory of the Median filter is to supersede the gray value of pixels with the median of the gray value in a neighborhood of the pixels, and not use the average proportion. The Mean filter is a sort of plain sliding-window space filter that takes the place of the central value with the mean of all the pixel values in the plain window. This essay takes advantage of Matlab to complete the process of adding Gaussian noise in photos, reducing Gaussian noise, and calculating the PSNR. The processing result of two types of noise reducers on Gaussian noise is compared. And by observing the image clarity, PSNR and MSE evaluation techniques to find which is a better filter to decrease Gaussian noise.

A simplified protocol of regional citrate anticoagulation with phosphate-containing solutions in infants and children treated with continuous kidney replacement therapy.

Regional citrate anticoagulation (RCA) is the preferred modality of anticoagulation used in continuous kidney replacement therapy (CKRT) in adults and less extensively in children. Potential metabolic complications limit widespread use in infants, neonates, and in children with liver failure. We report our experience with a simplified protocol in 50 critically ill children, infants, and neonates, some of them with liver failure, with commercially available solutions containing phosphorous and higher concentration of potassium and magnesium. RCA allowed attainment of a mean filter lifetime of 54.5 ± 18.2h, 42.5% of circuits lasted more than 70h, and scheduled change was the most frequent cause of CKRT interruption. Patient Ca++ and circuit Ca++ were maintained in the target range with mean values of 1.15 ± 0.13mmol/l and 0.38 ± 0.07mmol/l, respectively. No session had to be stopped because of metabolic complications. The most frequent complications were hyponatremia, hypomagnesemia, and metabolic acidosis mostly related to primary disease and critical illness. No session had to be stopped because of citrate accumulation (CA). Transitory CA occurred in 6 patients and was managed without requiring RCA interruption. No patients with liver failure presented CA episodes. In our experience, RCA with commercially available solutions was easily applied and managed in critically ill children, even in patients with low weight or with liver failure. Solutions containing phosphate and higher concentrations of magnesium and potassium allowed reduction of metabolic derangement during CKRT. Prolonged filter life was ensured with no detrimental effects on patients and reduced staff workload. A higher resolution version of the Graphical abstract is available as Supplementary information.

Image Denoising using Mean Filter

The first step in any image processing process is to remove noise from an image, and this is the aim of every de-noising technique. The noise removal technique should be used carefully to avoid introducing artifacts that could cause the image to become blurry. In this paper, five levels of Gaussian noise and Salat & Peppers are applied to original images to add noise (σ=5, σ=10, σ=15, σ=20, σ=25 σ=50) and also combination Gaussian and Salt & peepers are used with noise level 25. Removing noising is filtered in mean filter. Performance evaluation of the mean filter with three methods of noise is provided. After testing all the approaches with Gaussian noise σ =15, the results revealed that Lena's image was superior to the others. The other photos, however, performed better for the Gaussian levels 20 and 25 than the Lena image. With all types of noise, the mean filter produced good results.

Seismic Retrofit of Pilotis Buildings by Novel Aluminium Buckling-Restrained Braces (Al-BRBs). Application to a Modernist Architecture Building in Lisbon

ABSTRACT The worldwide dissemination of the pilotis multi-storey RC buildings, generally attributed to the architect Le Corbusier, was contemporary with a structural engineering line of thought that advocated a soft first storey as a means of filtering down the seismic inertia forces in the upper storeys. These two architectural and structural international trends, combined with outdated detailing and low-code seismic design, greatly increase the risks of developing a soft storey sidesway mechanism at the first storey, as shown in past earthquake occurrences and other studies. The article explores the possibility of strengthening a representative 1955 pilotis building in Lisbon by the inclusion of novel aluminium buckling-restrained braces (Al-BRBs) at the first soft storey. This should reduce the extreme deformation concentration at that level without fully eliminating it, and thus take advantage of the additional damping provided by the stable hysteretic behaviour of the Al-BRBs. These Al-BRBs have some distinctive features that take advantage of the unique properties of the aluminium alloys, such as the formability (allowing for an extruded casing component) and the possibility of improving the low-cycle fatigue and deformation characteristics of the core component by means of thermal treatments.

Segmentation of renal structures based on contrast computed tomography scans using a convolutional neural network

Aim. Develop a neural network to build 3D models of kidney neoplasms and adjacent structures. Materials and methods. DICOM data (Digital Imaging and Communications in Medicine standard) from 41 patients with kidney neoplasms were used. Data included all phases of contrast-enhanced multispiral computed tomography. We split the data: 32 observations for the training set and 9 – for the validation set. At the labeling stage, the arterial, venous, and excretory phases were taken, affine registration was performed to jointly match the location of the kidneys, and noise was removed using a median filter and a non-local means filter. Then the masks of arteries, veins, ureters, kidney parenchyma and kidney neoplasms were marked. The model was the SegResNet architecture. To assess the quality of segmentation, the Dice score was compared with the AHNet, DynUNet models and with three variants of the nnU-Net (lowres, fullres, cascade) model. Results. On the validation subset, the values of the Dice score of the SegResNet architecture were: 0.89 for the normal parenchyma of the kidney, 0.58 for the kidney neoplasms, 0.86 for arteries, 0.80 for veins, 0.80 for ureters. The mean values of the Dice score for SegResNet, AHNet and DynUNet were 0.79; 0.67; and 0.75, respectively. When compared with the nnU-Net model, the Dice score was greater for the kidney parenchyma in SegResNet – 0.89 compared to three model variants: lowres – 0.69, fullres – 0.70, cascade – 0.69. At the same time, for the neoplasms of the parenchyma of the kidney, the Dice score was comparable: for SegResNet – 0.58, for nnU-Net fullres – 0.59; lowres and cascade had lower Dice score of 0.37 and 0.45, respectively. Conclusion. The resulting SegResNet neural network finds vessels and parenchyma well. Kidney neoplasms are more difficult to determine, possibly due to their small size and the presence of false alarms in the network. It is planned to increase the sample size to 300 observations and use post-processing operations to improve the model.

BLE beacon-based floor detection for mobile robots in a multi-floor automation laboratory

Abstract As an important task of multi-floor localization, floor detection has elicited great attention. Wireless infrastructures like Wi-Fi and Bluetooth Low Energy (BLE) play important roles in floor detection. However, most floor detection research tends to focus on data modelling but pays little attention to the data collection system, which is the basis of wireless infrastructure-based floor detection. In fact, the floor detection task can be greatly simplified with proper data collection system design. In this paper, a floor detection solution is developed in a multi-floor life science automation lab. A data collection system consisting of BLE beacons, a receiver node and an Internet of Things (IoT) cloud is provided. The features of the BLE beacon under different settings are evaluated in detail. A mean filter is designed to deal with the fluctuation of the received signal strength indicator data. A simple floor detection method without a training process was implemented and evaluated in more than 100 floor detection tests. The time delay and floor detection accuracy under different settings are discussed. Finally, floor detection is evaluated on the H20 multi-floor transportation robot. Two sensor nodes are installed on the robot at different heights. The floor detection performance with different installation heights is discussed. The experimental results indicate that the proposed floor detection method provides floor detection accuracy of 0.9877 to 1 with a time delay of 5 s.

MRI Brain Tumor Classification Using Computational Intelligence Technique

We proposed a method for detecting and classifying tumors using parallel processing, soft computing, and machine learning techniques. Due to the augmented size and capacity of medical images, the medical imaging field must need an automatic diagnosis process for tumor classification for improved treatment. Initially, we pre-process Magnetic Resonance Imaging (MRI) brain tumor images using non-local adaptive means filter and contrast enhancement in the proposed system. We use a Fuzzy Clustering Means (FCM) algorithm to segment the tumors from the preprocessed MRI brain images. The significant features are obtained from the segmented tumor images by using Discrete Wavelet Transform (DWT), Principal Components Analysis (PCA), and the gray level co-occurrence matrix (GLCM). Machine learning classifiers are peerless techniques for classifying tumors. The classification of tumor grade is done through the Multiclass Support vector machine (MCSVM) classifier. The entire proposed work is implemented on the NVIDIA GeForce GTX parallel processing environment. The innovative final result of our approach was assessed by the Kaggle dataset with the performance metrics and achieved 98.056 % Sensitivity, 100 % Specificity, and 98.139 % Accuracy in 0.3337 computational times

Accurate detection of brain tumor using optimized feature selection based on deep learning techniques.

An unusual increase of nerves inside the brain, which disturbs the actual working of the brain, is called a brain tumor. It has led to the death of lots of lives. To save people from this disease timely detection and the right cure is the need of time. Finding of tumor-affected cells in the human brain is a cumbersome and time- consuming task. However, the accuracy and time required to detect brain tumors is a big challenge in the arena of image processing. This research paper proposes a novel, accurate and optimized system to detect brain tumors. The system follows the activities like, preprocessing, segmentation, feature extraction, optimization and detection. For preprocessing system uses a compound filter, which is a composition of Gaussian, mean and median filters. Threshold and histogram techniques are applied for image segmentation. Grey level co-occurrence matrix (GLCM) is used for feature extraction. The optimized convolution neural network (CNN) technique is applied here that uses whale optimization and grey wolf optimization for best feature selection. Detection of brain tumors is achieved through CNN classifier. This system compares its performance with another modern technique of optimization by using accuracy, precision and recall parameters and claims the supremacy of this work. This system is implemented in the Python programming language. The brain tumor detection accuracy of this optimized system has been measured at 98.9%.