Wiener Filter Research Articles

Manta Ray Foraging Optimization Transfer Learning-Based Gastric Cancer Diagnosis and Classification on Endoscopic Images.

Gastric cancer (GC) diagnoses using endoscopic images have gained significant attention in the healthcare sector. The recent advancements of computer vision (CV) and deep learning (DL) technologies pave the way for the design of automated GC diagnosis models. Therefore, this study develops a new Manta Ray Foraging Optimization Transfer Learning technique that is based on Gastric Cancer Diagnosis and Classification (MRFOTL-GCDC) using endoscopic images. For enhancing the quality of the endoscopic images, the presented MRFOTL-GCDC technique executes the Wiener filter (WF) to perform a noise removal process. In the presented MRFOTL-GCDC technique, MRFO with SqueezeNet model is used to derive the feature vectors. Since the trial-and-error hyperparameter tuning is a tedious process, the MRFO algorithm-based hyperparameter tuning results in enhanced classification results. Finally, the Elman Neural Network (ENN) model is utilized for the GC classification. To depict the enhanced performance of the presented MRFOTL-GCDC technique, a widespread simulation analysis is executed. The comparison study reported the improvement of the MRFOTL-GCDC technique for endoscopic image classification purposes with an improved accuracy of 99.25%.

Efficient, accurate and fast pupil segmentation for pupillary boundary in iris recognition

Iris recognition is a robust biometric system—user-friendly, accurate, fast, and reliable. This biometric system captures information in a contactless manner, making it suitable for use during the COVID-19 pandemic. Despite its advantages such as high security and high accuracy, iris recognition still suffers from pupil deformation, motion blur, eyelids blocking, reflection occlusion and eyelashes obscure. If the pupillary boundary is not accurately segmented, iris recognition may suffer tremendously. Moreover, reflections in iris image may lead to an incorrect pupillary boundary segmentation. The segmentation accuracy can also be affected and reduced because of the presence of an unwanted noise created by the motion blur effect in iris image. Additionally, the pupillary boundary might change from circular shape to uneven or irregular shape because of the interference and obstruction in pupil region. Therefore, this work is carried out to determine an accurate, efficient and fast algorithm for the segmentation of pupillary boundary. First, the iris image is pre-processed with Wiener filter. Next, the respective iris image is assigned with a specific threshold. After that, the pixel property in iris image is computed to determine the pupillary boundary coordinates which are acquired from the measured pixel list and area in iris image. Finally, morphological closing is used to remove reflections in the inner region of pupil boundary. All experiments are implemented with CASIA v4 database and Matlab R2020a.

Accelerating trail navigation for unmanned aerial vehicle: A denoising deep-net with 3D-NLGL

Waypoints have enhanced the prospect of fully autonomous drone applications. However, Geographical Position System (GPS) spoofing and signal interferences are key issues in waypoint-based drone applications. Also, conceptual waypoint-based drone applications require accurate awareness of waypoints based on environmental cues and integration of additional sensing modalities. Additional sensor modalities may overwhelm drones’ processing resources, reducing operational time. This study proposes W-MobileNet, a denoising model for autonomous trajectory trail navigation based on precision control of a path planner, denoising capabilities of Weiner filters, and perceptual knowledge of convolutional neural networks. Creatively integrating the modules of W-MobileNet results in an intuitive drone navigation controller characterized by position, orientation, and speed estimation. Further, a generic loss function that significantly aids models to converge faster during training is proposed based on adaptive weights. An extensive evaluation of a simulated and real-world experiment shows that W-MobileNet is more favorable in precision and robustness than contemporary state-of-the-art models. W-MobileNet has the potential to become one of the standards for autonomous drone applications.

Global Moho Gravity Inversion from GOCE Data: Updates and Convergence Assessment of the GEMMA Model Algorithm

Since its discovery in 1909, the Moho was routinely studied by seismological methods. However, from the 1950s, a possible alternative was introduced by gravimetric inversion. Thanks to satellite gravity missions launched from the beginning of the 21st century, a global inversion became feasible, e.g., leading to the computation of the GEMMA model in 2012. This model was computed inverting the GOCE second radial derivatives of the anomalous potential by a Wiener filter, which was applied in the spherical harmonic domain, considering a two-layer model with lateral and vertical density variations. Moreover, seismic information was introduced in the inversion to deal with the joint estimation/correction of both density and geometry of the crustal model. This study aims at revising the GEMMA algorithm from the theoretical point of view, introducing a cleaner formalization and studying the used approximations more thoroughly. The updates are on: (1) the management of the approximations due to the forward operator linearization required for the inversion; (2) the regularization of spherical harmonic coefficients in the inversion by proper modelling the Moho signal and the gravity error covariances; (3) the inclusion of additional parameters and their regularization in the Least Squares adjustment to correct the density model by exploiting seismic information. Thanks to these updates, a significant improvement from the computational point of view is achieved too, thus the convergence of the iterative solution and the differences with respect to the previous algorithm can be assessed by closed-loop tests, showing the algorithm performance in retrieving the simulated “true” Moho.

Low coherency of wind induced seismic noise: implications for gravitational wave detection

Seismic noise poses challenges for gravitational wave detection. Effective vibration isolation and methods to subtract unshieldable Newtonian noise (NN) are examples. Seismic arrays offer one way to deal with these issues by making use of correlations between seismic ground measurements and noise inside the detector. In this paper we find that wind induced seismic noise is incoherent and our results show that it can dramatically reduce the projected low frequency sensitivity of future gravitational wave detectors. To quantify this, we measure the coherence length of wind induced seismic noise from 0.06–20 Hz in three distinct locations: close to a building, among tall trees and in shrubs. We show that wind induced seismic noise is ubiquitous and reduces the coherence lengths from several hundred meters to 2–40 m for 0.06–0.1 Hz, from 60 m to 3–16 m for 1.5–2.5 Hz and from 35 m to 1–16 m around 16.6 Hz in the study area. This leads to significant loss of velocity resolution of the array for primary microseism and 5 times worse NN cancellation by Wiener filtering at 2 Hz, while it may not pose additional limitation to NN cancellation between 10–20 Hz.

Artificial intelligence neural network analysis and application of CT imaging features to predict lymph node metastasis in non-small cell lung cancer.

Computed tomography (CT) is important in the diagnosing of lung cancer. The combination of CT features and artificial intelligence algorithm have been used in the diagnosis of various lung diseases. However, limited studies focused on the relationship between the combination of CT features and artificial intelligence algorithm and lymph node metastasis in non-small cell lung cancer (NSCLC). This study developed an algorithm for lung cancer CT image segmentation based on an artificial neural network model and investigated the role of a nomogram model based on CT images for predicting lymph node metastasis in lung cancer. Wiener filtering and fuzzy enhancement were first used to suppress image noise and improve image contrast. Next, texture features and fractal features were extracted. In the third step, the artificial neural network model was trained and tested according to the best parameters of the network. The area under the curve (AUC) of the constructed nomogram model on the training set and the test set were 0.859 (sensitivity, 0.810; specificity, 0.773) and 0.864 (sensitivity, 0.820; specificity, 0.753), respectively. The decision curve indicated that the model had good clinical application value. The lung cancer CT images contained 13 significant regional features of cancer. The best classification function obtained from training and testing data was Levenberg-Marquardt backpropagation. The sensitivity, specificity, and accuracy in the training stage could reach 98.4%, 100%, and 98.6%, respectively, and the corresponding indexes in the test stage reached 90.9%, 100%, and 95.1%, respectively. The image segmentation algorithm based on the artificial neural network model could extract CT lung cancer lesions efficiently and quasi-determinately, which could be used as an effective tool for radiologists to diagnose lung cancer. The nomogram model based on CT image features and related clinical indicators was an effective method for noninvasive prediction of lymph node metastasis in lung cancer.

Cocktail parity problem solution based on modified blind extraction technique

The Cocktail Party Problem solution is described as being the responsibility of isolating the voice signal in a noisy environment. Two popular methods for resolving this issue are blind source extraction and the Wiener filtering procedure. The blind source extraction approaches like fastICA, JADE and efficient fastICA are useful for extraction data from the mixed signals. The classical optimization techniques such as genetic algorithms or particle swarms for blind source extraction are mostly founded on the gradient and need the objective function, so the using of these techniques is very restricted. In the recent studies, the classical blind source separation techniques will not give the perfect solution for the cocktail parity problem. These methods struggle with convergence speed and accuracy issues as well. In order to enhance the separation process and get over these issues, this work adopted the glowworm swarm technique based on kurtosis as the objective function. The results show that the proposed technique produces good separation. The original and estimated signals are compared for similarity using the cross-correlation function.

Liver Tumor Classification Using Optimal Opposition-Based Grey Wolf Optimization

Image processing plays a significant role in various fields like military, business, healthcare and science. Ultrasound (US), Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the various image tests used in the treatment of the cancer. Detecting the liver tumor by these tests is a complex process. Hence, in this research work, a novel approach utilizing a deep learning model is used. That is Deep Belief Network (DBN) with Opposition-Based Learning (OBL)-Grey Wolf Optimization (GWO) is used for the classification of liver cancer. This process undergoes five major processes. Initially, in pre-processing the color contrast is improved by Contrast Limited Adaptive Histogram Equalization (CLAHE) and the noise is removed by Wiener Filtering (WF). The liver is segmented by adaptive thresholding following pre-processing. Following that, the kernelizedFuzzy C Means (FCM) method is used to segment the tumor area. The form, color, and texture features are then extracted during the feature extraction process. Finally, these traits are categorized using DBN, and OBL-GWO is employed to enhance system performance. The entire evaluation is done on Liver Tumor Segmentation (LiTS) benchmark dataset. Finally, the performance of the proposed DBN-OBL-GWO is compared to other models and their achievements are proved. The proposed DBN-OBL-GWO achieves a better accuracy of 0.995, precision of 0.948 and false positive rate (FPR) of 0.116, respectively.

Study on the Extraction of CT Images with Non-Uniform Illumination for the Microstructure of Asphalt Mixture.

An adaptive image-processing method for CT images of asphalt mixture is proposed in this paper. Different methods are compared according to the error analysis calculated between the real gradation and 3D reconstruction gradation. As revealed by the test results, the adaptive image-processing method was effective in carrying out different brightness homogenization processes for each image. The Wiener filter with 7 × 7 size filter was able to produce a better noise reduction effect without compromising image sharpness. Among the three methods, the adaptive image-processing method performed best in the accuracy of coarse aggregate recognition, followed by the ring division method and the global threshold segmentation method. The error of the gradation extracted by the adaptive image-processing method was found to be lowest compared with the real gradation. For a variety of engineering applications, the developed method helps to improve the analysis of CT images of asphalt mixtures.

Pixel-based approach to delay multiply and sum beamforming in combination with Wiener filter for improving ultrasound image quality

Unified pixel-based (PB) beamforming has been implemented for ultrasound imaging, offering significant enhancements in lateral resolution compared to the conventional dynamic focusing. However, it still suffers from clutter and off-axis artifacts, limiting the contrast resolution. This paper proposes an efficient method to improve image quality by integrating filtered delay multiply and sum (F-DMAS) into the framework. This hybrid strategy incorporates the spatial coherence of the received data into the beamforming process to improve contrast resolution and clutter rejection in the generated image. We also integrate a Wiener filter to suppress the spatiotemporal spreading using signals echoed from a single scatterer at the transmit focus as a kernel for the deconvolution. The Wiener filter is applied to the received waveforms before performing the hybrid strategy. The Wiener filter is shown to reduce interference due to the interaction between the excitation pulse and the transfer functions of the transducer elements, thus benefiting the axial resolution of the generated images. We validate the proposed method and compare it with other beamforming strategies through a series of experiments, including simulation, phantom, and in vivo studies. The results show that our approach can substantially improve both spatial resolution and contrast over the unified PB algorithm, while still maintaining the good features of this beamformer. The simplicity and good performance of our method show its potential for use in clinical applications.

Multimodal Brain Image Fusion Based on Improved Rolling Guidance Filter and Wiener Filter.

Medical image fusion technology can integrate complementary information from different modality medical images to provide a more complete and accurate description of the specific diagnosed object, which is very helpful for image-guided clinical diagnosis and treatment. This paper proposes an effective brain image fusion framework based on improved rolling guidance filter (IRGF). Firstly, input images are decomposed into base layers and detail layers using the IRGF and Wiener filter. Secondly, the visual saliency maps of the input image are computed by pixel-level saliency value, and the weight maps of detail layers are constructed by max-absolute strategy and are further smoothed with Gaussian filter, the purpose of which is to make the fused image appear more naturally and more suitable for human visual perception. Lastly, base layers are fused by visual saliency map based fusion rule and the corresponding weight maps from detail layers are fused by the weighted least squares optimization scheme. Experimental results testify that our method is superior to some state-of-the-art methods in both subjective and objective assessments.

Improved MAX-MIN method to estimate total magnetization direction using Wiener filtering

Knowledge of the total magnetization direction is useful for forward modeling and reduction-to-the-pole (RTP) of magnetic anomalies. However, remanent magnetization deviates the total magnetization direction from the induced magnetization direction; therefore, it is essential to determine the correct direction of total magnetization. Theoretically, the MAX-MIN method, which searches for the position of the maximum value of the function that comprises the minimum values of the RTP field with different directions, can yield the correct direction of total magnetization. However, such estimations of direction usually indicate large errors in comparison with the results of crosscorrelation methods, which calculate the correlation coefficient between the RTP field and the magnetic magnitude transforms. Our analysis reveals that noise is the primary cause of the inaccuracy in estimation results. We have improved the MAX-MIN method using Wiener filtering based on the principle of least-mean-squares deviation. The k-means clustering algorithm clusters points that are as closely connected as possible, while ensuring that the distances between clusters are as large as possible. Therefore, the k-means clustering algorithm is used to divide the radial logarithmic power spectrum into the effective signal with low wavenumbers and the noisy signal with high wavenumbers. Then, the Wiener filtering RTP operator is constructed using the slope and intercept of the fitted straight line in the corresponding wavenumber bands. The improved MAX-MIN method is tested using magnetic anomalies of spherical and combined cuboid models with different levels of added Gaussian noise and different total magnetization directions. The improved MAX-MIN method is applied to the magnetic anomaly in the Yeshan region (eastern China) and the Taihe region (southwestern China). Results indicate that the improved MAX-MIN method based on the principle of least-mean-squares deviation could obtain reliable estimation of direction from noisy magnetic anomaly data.

Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram.

The electrohysterogram (EHG) is the uterine muscle electromyogram recorded at the abdominal surface of pregnant or non-pregnant woman. The maternal respiration electromyographic signal (MR-EMG) is one of the most relevant interferences present in an EHG. Alvarez (Alv) waves are components of the EHG that have been indicated as having the potential for preterm and term birth prediction. The MR-EMG component in the EHG represents an issue, regarding Alv wave application for pregnancy monitoring, for instance, in preterm birth prediction, a subject of great research interest. Therefore, the Alv waves denoising method should be designed to include the interference MR-EMG attenuation, without compromising the original waves. Adaptive filter properties make them suitable for this task. However, selecting the optimal adaptive filter and its parameters is an important task for the success of the filtering operation. In this work, an algorithm is presented for the automatic adaptive filter and parameter selection using synthetic data. The filter selection pool comprised sixteen candidates, from which, the Wiener, recursive least squares (RLS), householder recursive least squares (HRLS), and QR-decomposition recursive least squares (QRD-RLS) were the best performers. The optimized parameters were L = 2 (filter length) for all of them and λ = 1 (forgetting factor) for the last three. The developed optimization algorithm may be of interest to other applications. The optimized filters were applied to real data. The result was the attenuation of the MR-EMG in Alv waves power. For the Wiener filter, power reductions for quartile 1, median, and quartile 3 were found to be −16.74%, −20.32%, and −15.78%, respectively (p-value = 1.31 × 10−12).

Auto Segmentation of Lymph Node Microscopy Images

The manual histology assessment on the biopsy tissue sample still remains the gold standard procedure for cancer and its progression in human body. Auto nuclei segmentation is an important method to measure cellularity but often suffered an issue due to the present of overlapping nuclei. The implementation of auto segmentation of cells could speed up the process of histology assessment for cancer cases. The first step to implement, a wide data profile of normal and cancerous need to be compile and analyze further as a reference guide. Tissue data profile can be collected based on cellularity property of the tissue which can be automatically segmented using MATLAB software. The objective of the study is to develop an auto nuclei segmentation using MATLAB software to measure cellularity between normal and cancerous cells of lymph node tissue. Histological images of the tissue were analyzed using MATLAB software by using thresholding method and the result was compared with ImageJ. The pre-processing part of the image processing incudes converting the image into 8-bit grayscale image. The segmentation parts include adaptive filtering to remove the noise using Wiener filter and the thresholding Otsu method. Results from the ImajeJ and manual counting on the cellularity shows a comparable results to the automated cellularity measured using MATLAB. The cellularity of the cancerous lymph nodes was found lower than normal lymph nodes.

Using Discrete Wavelet Transform and Wiener filter for Image De-nosing

In this paper we proposed an algorithm for image de-nosing based on; the two level discrete wavelet transform (DWT), and Wiener filter, also this paper describe estimate noise power. At first The DWT transform noisy image into sub-bands, consist of lowfrequencyand high-frequencies, and then estimate noise power for each sub-band. The noise power is computed through two important computations; compute square of variance for each sub-band then compute the mean of the variance. After compute the variance apply the wiener filter on each sub-band by using local window nn , finally perform inverse DWT to obtain de-noised image. Our algorithm tested on the two color images and also compared with Normal Shrink filter and Wiener filter.

Genetic Algorithm-Based Adaptive Wiener Gain for Speech Enhancement Using an Iterative Posterior NMF

In this paper, we propose a genetic algorithm-based adaptive Wiener gain for speech enhancement using an iterative posterior non-negative matrix factorization (NMF). In the recent past, NMF-based Wiener filtering methods were used to improve the performance of speech enhancement, which has shown that they provide better performance when compared with conventional NMF methods. But performance degrades in non-stationary noise environments. Template-based approaches are more robust and perform better in non-stationary noise environments compared to statistical model-based approaches but are dependent on a priori information. Combining the approaches avoids the drawbacks of both. To improve the performance further, speech and noise bases are adapted simultaneously in the NMF approach. The usage of Super-Gaussian constraints in iterative NMF still improves the performance in non-stationary noise. The silence frame is a challenging task in the case of NMF; still there will be some amount of noise present in those frames. For further enhancement, we have combined with a genetic algorithm (GA)-based adaptive Wiener filter which performs well in denoising and also the GA search the adaptive [Formula: see text] allows us to control the trade-off between fitting the observed spectrogram of mixed speech and noise achieving high likelihood under our prior model. The proposed method outperforms other benchmark algorithms in terms of the source to distortion ratio (SDR), short-time objective intelligibility (STOI), and perceptual evaluation of speech quality (PESQ).

Research on Noise Suppression Technology of "Black Triangle" of Vibroseis Seismic Data

The "black triangle" noise, harmonic interference, and adjacent gun interference in the seismic data collected by the controllable vibrator seriously affect the signal-to-noise ratio of the seismic data. With the emergence of high-efficiency acquisition technology, the "black triangle" noise the impact is getting bigger and bigger. At present, the geological tasks are often accomplished through higher-level seismic data acquisition and indoor fine processing. In order to suppress the "black triangle" noise of controllable seismic data, the effective suppression of the "black triangle" noise has been achieved through the research of high-resolution time-frequency analysis, "black triangle" noise feature extraction, and Wiener filtering. The "black triangle" noise suppression processing of the actual controllable seismic data in the Shun 8 well area of the Tarim Basin has achieved good results. It is of great significance for optimizing the processing effect of the seismic data collected by the controllable seismic source and popularizing the scope of application of the controllable seismic source.

Research on Start-stop standby energy storage element participating in wind power filtering under the influence of power quality disturbance

In order to realize effective noise reduction of wind power under the influence of complex power quality disturbance(PQD), an application fusion method of improved smoothing and noise reduction strategy and start-stop energy storage element is proposed in this paper. Firstly, the complex power quality disturbance is filtered and de-noised based on Wiener filtering(WF) algorithm to obtain stable disturbance output. Then, a filtering self-built library(FSBL) is established to perform parallel operations of multiple filtering algorithms and obtain the optimal output power according to the constraints of time scale. After that, a hybrid energy storage system(HESS) with start-stop standby energy storage element is proposed. According to the maximum entropy difference drop point and permutation entropy test, the start-stop instructions of standby energy storage elements can be achieved. Finally, the state of charge(SOC) is optimized according to fuzzy control constraints. In order to verify the effectiveness of the proposed method, this paper constructs the wind power output based on the hardware experimental platform. The power quality disturbance database is further constructed to verify and analyze power quality disturbance types from simple to complex. The results show that the proposed method is reliable. And the hybrid energy storage system with start-stop standby energy storage elements can be widely used in many fields and scenarios.

DeepJoint Segmentation-based Lung Segmentation and Hybrid Optimization-Enabled Deep Learning for Lung Nodule Classification

Lung cancer is an aggressive disease among all cancer-based diseases, because of causing huge mortality in humans. Thus, earlier discovery is a basic task for diagnosing lung cancer and it helps increase the survival rate. Computed tomography (CT) is a powerful imaging technique used to discover lung cancer. However, it is time-consuming for examining each CT image. This paper develops an optimized deep model for classifying the lung nodules. Here, the pre-processing is done using Region of Interest (ROI) extraction and adaptive Wiener filter. The segmentation is done using the DeepJoint model wherein distance is computed with a congruence coefficient for extracting the segments. The nodule identification is done by a grid-based scheme. The features such as Global Binary Pattern (GBP), Texton features, statistical features, perimeter and area, barycenter difference, number of slices, short axis and long axis and volume are considered. The lung nodule classification is done to classify part solid, solid nodules and ground-glass opacity (GGO) using Deep Residual Network (DRN), which is trained by the proposed Shuffled Shepard Sine–Cosine Algorithm (SSSCA). The developed SSSCA is generated by the integration of the Sine–Cosine Algorithm (SCA) and Shuffled Shepard Optimization Algorithm (SSOA). The proposed SSSCA-based DRN outperformed with the highest testing accuracy of 92.5%, sensitivity of 93.2%, specificity of 83.7% and [Formula: see text]-score of 81.5%.

Spectral Methods for Single Channel Speech Enhancement in Multi-Source Environment

Speech communication for both humans and automatic devices can be negatively impacted by background noise, which is common in real environments. Among many techniques, speech separation using a single microphone is the most desirable from an application standpoint. The resulting monaural speech separation problem has been a central one in speech processing for several decades. However, its success has been limited thus far. This research presents work that develops speech separation systems using combinations of T-F masking, DNNs, and model-based reconstruction. The aim of each system is to improve the perceptual quality of the speech estimates. The performance of many speech processing applications is severely degraded when both noise and reverberation are present. The proposed solution has been tested in the simulation environment and based on the simulation result, it is observed that the speech enhancement can easily be performed through the integration of the solution. This research suggests two staged noise reducing systems in order to reduce the background noise through a single microphone recording in a low-SNR based on ideal binary masking and Wiener filter. It has two stages. Firstly, for background noise reduction, a Wiener filter with an enhanced SNR is utilised on noisy speech. Secondly, IBM is calculated in each time–frequency channel through utilisation of the pre–processed speech from the first stage and the matching of the time–frequency channels to a pre-selected threshold in order to minimise residual noise. These channels meeting the threshold requirement are conserved while all the other ones are attenuated.