Wiener Filtering Technique Research Articles

Segmenting and classifying lung diseases with M-Segnet and Hybrid Squeezenet-CNN architecture on CT images.

Diagnosing lung diseases accurately and promptly is essential for effectively managing this significant public health challenge on a global scale. This paper introduces a new framework called Modified Segnet-based Lung Disease Segmentation and Severity Classification (MSLDSSC). The MSLDSSC model comprises four phases: "preprocessing, segmentation, feature extraction, and classification." Initially, the input image undergoes preprocessing using an improved Wiener filter technique. This technique estimates the power spectral density of the noisy and original images and computes the SNR assisted by PSNR to evaluate image quality. Next, the preprocessed image undergoes Segmentation to identify and separate the RoI from the background objects in the lung image. We employ a Modified Segnet mechanism that utilizes a proposed hard tanh-Softplus activation function for effective Segmentation. Following Segmentation, features such as MLDN, entropy with MRELBP, shape features, and deep features are extracted. Following the feature extraction phase, the retrieved feature set is input into a hybrid severity classification model. This hybrid model comprises two classifiers: SDPA-Squeezenet and DCNN. These classifiers train on the retrieved feature set and effectively classify the severity level of lung diseases.

Statistically bias-minimized peculiar velocity catalogs from Gibbs point processes and Bayesian inference

The peculiar velocities of galaxies can serve as excellent cosmological probes provided that the biases inherent to their measurements are contained prior to the start of any study. This paper proposes a new algorithm based on an object point process model whose probability density is built to statistically reduce the effects of Malmquist biases and uncertainties due to lognormal errors in radial peculiar velocity catalogs. More precisely, a simulated annealing algorithm allows for the probability density describing the point process model to be maximized. The resulting configurations are bias-minimized catalogs. We conducted tests on synthetic catalogs mimicking the second and third distance modulus catalogs of the Cosmicflows project from which peculiar velocity catalogs are derived. By reducing the local peculiar velocity variance in catalogs by an order of magnitude, the algorithm permits the recovery of the expected one, while preserving the small-scale velocity correlation. It also allows for the expected clustering to be retrieved. The algorithm was then applied to the observational catalogs. The large-scale structure reconstructed with the Wiener-filter technique applied to the bias-minimized observational catalogs matches that of the local cosmic web well, as supported by redshift surveys of local galaxies. These new bias-minimized versions of peculiar velocity catalogs can be used as a starting point for several studies, from plausible estimations of the most probable value for the Hubble constant, H0, to the production of simulations constrained to reproduce the local Universe.

Texture and pixel intensity characterization-based image segmentation with morphology and watershed techniques

<p>Image segmentation is an image processing technique that concentrated on finding and locating the parts of an image such as objects and boundaries. The purpose of locating these parts is for use in further processing analysis of an image such as recognition tasks, and content-based image retrieval. This paper introduces the segmentation procedure using a proposed template of features with watershed or morphology operations. Features template based on segmentation process conveys pixels’ intensities property perceived by the threshold value of histogram representation and texture feature where the regions are characterized by their texture content using standard deviation (SD) filtering. Wiener filter and histogram equalization (HE) techniques are used as preprocessing operations to enhance the image quality. The edge detector operator is hybridized to boost the segmentation process. Some statistical metrics are used for assessing and analyzing the performance of the stages in the proposed work. As a result, this proposed template of features achieved more performance with watershed and morphology segmentation.</p>

Automatic Detection of Diabetic Hypertensive Retinopathy in Fundus Images Using Transfer Learning

Diabetic retinopathy (DR) is a complication of diabetes that affects the eyes. It occurs when high blood sugar levels damage the blood vessels in the retina, the light-sensitive tissue at the back of the eye. Therefore, there is a need to detect DR in the early stages to reduce the risk of blindness. Transfer learning is a machine learning technique where a pre-trained model is used as a starting point for a new task. Transfer learning has been applied to diabetic retinopathy classification with promising results. Pre-trained models, such as convolutional neural networks (CNNs), can be fine-tuned on a new dataset of retinal images to classify diabetic retinopathy. This manuscript aims at developing an automated scheme for diagnosing and grading DR and HR. The retinal image classification has been performed using three phases that include preprocessing, segmentation and feature extraction techniques. The pre-processing methodology has been proposed for reducing the noise in retinal images. A-CLAHE, DNCNN and Wiener filter techniques have been applied for the enhancement of images. After pre-processing, blood vessel segmentation in retinal images has been performed utilizing OTSU thresholding and mathematical morphology. Feature extraction and classification have been performed using transfer learning models. The segmented images were then classified using Modified ResNet 101 architecture. The performance for enhanced images has been evaluated on PSNR and shows better results as compared to the existing literature. The network is trained on more than 6000 images from MESSIDOR and ODIR datasets and achieves the classification accuracy of 98.72%.

Analysis and Comparison of Image Enhancement Techniques for Improving PSNR of Liver Image by Mask Filtering over Wiener Filtering

Aim: The major purpose of this project is to improve picture enhancement for human viewers in order to improve interpretability or perception of information in images, as well as to give better input for other automated image processing approaches.The scientific images are corrupted by noise, and the images as a whole deteriorate. Edges are suppressed, structural features are blurred, and obstructions are blurred, among other things. Mask Filter and Wiener Filter are two filtering techniques. Materials and methods: Different sources of liver images from the kaggle website were used in this study. The total sample size was computed using clinical.com and the sample sizes were (N=20) for Mask filtering and (N=20) for the Wiener filtering technique. The total number of samples was calculated to be 40 as a consequence. The PSNR was calculated using SPSS Software and a standard data set. Through Matlab coding, both a mask filter and a wiener filter technique were used to enhance liver images, as well as retrieving PSNR values for each image. Then, using the SPSS software, a comparison and analysis were performed. Results: In an image enhancement of the image processing pathway, Wiener filters technique shows the best performance by removing noise to improve PSNR of liver images than mask filtering. PSNR values are compared using IBM-SPSS software and an independent sample test. Between the mask filter and wiener filtering, there is a statistical difference. In comparison to mask filtering, the wiener filtering technique produced greater PSNR (78.3780dB) with (p=0.004) (62.9695dB). Conclusion: Wiener Filtering image enhancement technique provides high PSNR values for different sources of liver images than mask filtering Technique.

Analysis and Comparison of Image Enhancement Technique for Improving PSNR of Lung Images by Wiener Filtering over Histogram Equalization Technique

Aim: The aim of this study is to compare and analyze wiener filter algorithm for lung image enhancement over novel histogram equalization technique. Materials and Methods:In this research, different sources of lung images collected from the kaggle website were used. Samples were considered as (N=30) for median filtering and (N=30) for novel histogram equalization technique with total sample size calculated using clinical. com. As a result the total number of samples was calculated to be 60. Using SPSS Software and a standard data set, the PSNR was obtained. Both median filter and novel histogram equalization technique image enhancement were implemented on lung images through Matlab coding and also extracting PSNR values of each image. Then through SPSS software comparison and analysis has been made. Result:. The novel histogram equalization technique showed higher results of PSNR (67.2076dB) in comparison with wiener filtering (37.1558dB). It is observed that histogram algorithm performed better than the Wiener filter (p=0.04) by performing an independent sample t-test. Conclusion: Within this research study the histogram equalization image enhancement technique has greater PSNR value of lung images than in wiener filtering technique.

Mobile Topology Learning of a Linear Dynamic Network

In this paper, the problem of identifying the topology of a linear dynamic network using data acquired from a mobile agent is considered. Network topology learning is important in various areas where not only the direction of the node connections, but also the time-dependent characteristics of these connections need to be identified. Mobile-sensing related issues such as the measurement cost, however, have not yet been accounted for in the existing work. The proposed approach combines the Wiener filter technique with gradient-based optimization to minimize the total estimation error in the output (i.e., the node response), and then, the cost in terms of the total measurement time. A simulation example of identifying a 6-node network is presented to illustrate the proposed approach. The simulation results show that for the network of inter-node dynamics of a wide range of characteristics (time constants), both the direction, existence, and dynamics of the inter-node connections can be accurately identified by using the proposed approach.

Robust Linearly Constrained Kalman Filter for General Mismatched Linear State-Space Models

It is well known that Wiener filter and Kalman filter (KF) like techniques are sensitive to misspecified covariances, uncertainties in the system matrices, filter initialization, or unwanted system behaviors. A possible solution to robustify these estimation techniques is to impose linear constraints (LCs). In this article: 1) we introduce a general class of linearly constrained KF (LCKF), where a set of nonstationary LCs can be set at every time step; 2) explore the use of such LCs to mitigate modeling errors in general mismatched linear discrete state-space models; and 3) provide the theoretical formulation to show that the gain-constrained KF is a particular instance of the proposed LCKF. Because such LCs can be taken into account in any KF generalization, this sets the basis for a new robust filtering framework. An illustrative example is provided to support the discussion.

Time-frequency readability enhancement of compact support kernel-based distributions using image post-processing: Application to instantaneous frequency estimation of M-ary frequency shift keying signals

Time-frequency distributions (TFDs) based on time-lag kernels with compact support (KCS) have proved their high performance in terms of resolution and crossterms suppression. However, as for all kernel-based quadratic TFDs, these distributions suffer from spreading out signal terms. This is due to the unavoidable smoothing effects of the kernel in the ambiguity domain. The main objective of this paper is to improve concentration, interference rejection and so time-frequency localization of this representation class. The latter has the advantage of being tuned using a single parameter while external windows are no longer needed. The KCS-TFDs, referred to as KCSDs, are first optimized using a selection of the most used objective performance measures in the literature. Important signal features are extracted as well through analysis of time slice plots. The obtained TF diagrams are then enhanced using a specific method that includes two-dimensional Wiener filter, automatic binarization and morphological image processing techniques. The enhanced plots are compared to those obtained from the original TFDs using several tests on real-life and multicomponent frequency modulated (FM) signals including the noise effects. Moreover, a comparative study involving a selection of the best-performing reassignment time-frequency distributions is provided. The obtained results show a significant improvement of concentration, time-frequency localization of the autoterms as well as interference and noise suppression. As viable applications, the proposed approach is used first to instantaneous frequency (IF) estimation of several synthetic and real-life M-ary frequency shift keying (MFSK) signals. It is shown that the IF estimator from the enhanced plots performs better than smoothed pseudo Wigner-Ville distribution (SPWVD) and reassignment post-processing-based TFDs in terms of mainlobe width (MLW) and variance, respectively, even at low signal-to-noise ratio (SNR). On the other hand, time-frequency characterization of continuous wave linear frequency modulation (CW-LFM) and pulse linear FM (PLFM) radar signals is also investigated.

Performance and quality assurance of medical image using hybrid thresholding wavelet transform with Wiener filter

ABSTRACT Medical images like X-ray, computed tomography, ultrasound, and magnetic resonance imaging (MRI) are produced using different techniques; during this process, noise is added that decreases the image quality and image analysis. Image denoising is an important task in image processing; use of wavelet transform improves the quality of an image and reduces noise level. We propose in this research, a denoising approach basing on discrete wavelet transform (DWT) using Hybrid Thresholding (bayesShrink) with Wiener filter technique for enhancing the quality image. This technique improved a better balance between smoothness and accuracy than the traditional wavelet DWT and are less redundant than stationary wavelet transform (SWT). In addition, the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) were used to analyse the denoised images quality.

Analysis and Prognosis of Failures in Intelligent Hybrid Systems Using Bioengineering: Gear Coupling

This work is of multidisciplinary concept, whose development is difficult to perform. Considering also that, in one of the steps, the similarity between the FRF of the vibration and acoustic signal is demonstrated. The objective of this work is the analysis and prognosis of the progression of failures of a pair of gears using the artificial immune system (AIS) of negative selection. In order to have this condition met, during the development of this work, the Wiener filter technique, the vibration and acoustic signal analysis (FRF), the application of negative selection AIS techniques for classification and grouping of signals were applied. The final result successfully demonstrates the effectiveness of the development process of this work and the robustness of the negative selection AIS algorithm.

Eye Blink Artefact Removal of Single Frontal EEG Channel Algorithm using Ensemble Empirical Mode Decomposition and Outlier Detection

Recently, the emergence of various applications to use EEG has evolved the EEG device to become wearable with fewer electrodes. Unfortunately, the process of removing artefact becomes challenging since the conventional method requires an additional artefact reference channel or multichannel recording to be working. By focusing on frontal EEG channel recording, this paper proposed an alternative single-channel eye blink artefact removal method based on the ensemble empirical mode decomposition and outlier detection technique. The method removes the segment of the potential eyeblinks artefact on the residual of a pre-determined level of decomposition. An outlier detection technique is introduced to identify the peak of the eyeblink based on the extreme value of the residual signal. The results showed that the corrected EEG signal achieved high correlation, low RMSE and have small differences in PSD when compared to the reference clean EEG. Comparing with an adaptive Wiener filter technique, the corrected EEG signal by the proposed method had better signal-to-artefact ratio.

Locust based genetic classifier for the diagnosis of diabetic retinopathy

Due to the ongoing advancement in detection of critical diseases, there is a need in revamping the accurate diagnosis of diabetic retinopathy (DR). In this current study, locust based genetic classifier plays a crucial role in early screening of DR and to determine the exact location of the affected region of retina. Initially, preprocessing is performed to remove the obnoxious information such as noise present in the image and helps to transform the RGB format to gray scale image. It is done by applying wiener filter technique. After removing the obnoxious information, exudate segmentation is performed. After splitting out the image into samples, feature extraction is applied by Gabor based region covariance matrix. It helps to reduce the feature in the DIARETDB1 dataset by obtaining a new feature. After obtaining the feature, whale optimization is performed to pick out the best features such as mean, exudate area, optic distance and standard deviation and finally locust based genetic classifier is used to analogize between trained and test set data and it provides infallible information to the ophthalmologist to provide timely treatments. Comparative analysis is performed. It reveals the significant performance of the current approach over other existing SVM and CNN classifier. The results obtained from the current study shows a promising future and it achieves an accuracy of 98.9%.

Noise Deduction in Novel Paddy Data Repository using Filtering Techniques

Classification of paddy crop diseases in prior knowledge is the current challenging task to evolve the economicgrowth of the country. In image processing techniques, the initial process is to eliminate the noise present in the dataset. Removing the noise leads to improvements in the quality of the image. Noise can be removed by applying filtering techniques. In this paper, a novel data repository created from different paddy areas in Vellore, which includes the following diseases, namely Bacteria Leaf Blight, Blast, Leaf Spot, Leaf Holder, Hispa and Healthy leaves. In the initial process, three kinds of noises, namely Salt and Pepper noise, Speckle noise, and Poisson noises, were removed using noise filtering techniques, namely Median and Wiener filter. Theinterpretation made over the median and Wiener filtering techniques concerning noises, the performance of the methods measured using metrics namely PSNR (peak to signal to noise ration), MSE (mean square error), Maxerr (Maximum squared error), L2rat (ratio of squared error). It is observed that the PSNR value of the hybrid approach is 18.42dB, which produces less error rate as compared with the traditional approach. Results suggest that the methods used in this paper are suitable for processing noise.

A tomographic map of the large-scale matter distribution using the eBOSS—Stripe 82 Lyα forest

The Lyman-α (hereafter Lyα) forest is a probe of large-scale matter density fluctuations at high redshift, z > 2.1. It consists of H I absorption spectra along individual lines-of-sight. If the line-of-sight density is large enough, 3D maps of H I absorption can be inferred by tomographic reconstruction. In this article, we investigate the Lyα forest available in the Stripe 82 field (220 deg2), based on the quasar spectra from SDSS Data Release DR16. The density of observed quasar spectra is 37 deg−2 with a mean pixel signal-to-noise ratio of two per angstrom. This study provides an intermediate case between the average SDSS density and that of the much denser but smaller CLAMATO survey. We derive a 3D map of large-scale matter fluctuations from these data, using a Wiener filter technique. The total volume of the map is 0.94 h−3 Gpc3. Its resolution is 13 h−1 Mpc, which is related to the mean transverse distance between nearest lines-of-sight. From this map, we provide a catalog of voids and protocluster candidates in the cosmic web. The map-making and void catalog are compared to simulated eBOSS Stripe 82 observations. A stack over quasar positions provides a visualization of the Lyα forest-quasar cross-correlation. This tomographic reconstruction constitutes the largest-volume high-redshift 3D map of matter fluctuations.

Enhanced Mammography image for Breast cancer detection using LC-CLAHE technique

Breast cancer is the greatest challenging health complexities that medical science is facing. Most cases can be prevented by early detection and diagnosis which are the best way to cure breast cancer to decrease the mortality rate. The aim of this research is to obtain a method for enhancing the mammography images by using the proposed method which is incorporating the Local Contrast with Contrast Limited Adaptive Histogram Equalization (LC-CLAHE) to improve the appearance and to increase the contrast of the image and then de-noised by 2D wiener filter techniques. To extract the region of interest (tumor), we used region growing technique for the segmentation process. The standard Mammographic Image Analysis Society (MIAS) database images are considered for the evaluation. Efficiency is measured by Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR). It is observed that the proposed method with wiener filter gives higher (PSNR) and lower (RMSE), with a significant filter mask [3 3].

Median modified wiener filter for improving the image quality of gamma camera images

The filter technique was applied to noise images, as noise is the significant factor that cause poor image quality due to lower photon counting. The purpose of this study is to confirm that image quality can be improved using the median modified Wiener filter (MMWF) technique; this is achieved via a National Electrical Manufacturers Association International Electrotechnical Commission body phantom with four large spheres that are filled with the 99mTc radioisotope when evaluating the image quality. Conventional filters such as Wiener, Gaussian, and median filters were designed, and signal to noise ratio, coefficient of variation, and contrast to noise ratio were used as the evaluation parameters. The improvement in the image quality was in the following order, from the least to the highest improvement, in all cases: Wiener filter, Gaussian filter, median filter, and the MMWF technique. The results show that the image quality was improved from 20.6 to 65.5%, 7.4–40.3%, and 12.7–44.7% for the SNR, COV, and CNR values, respectively, when using the MMWF technique, compared with the use of conventional filters. In conclusion, our results demonstrated that the MMWF technique is useful for reducing the noise distribution in gamma camera images.

A Study on Digital Image Restoration Filters

In this paper, at first a color image is taken Then the image is transformed into a grayscale image. After that, the motion blurring effect is applied to that image according to the image degradation model described in equation 3.the blurring effect can be controlled by a and b components of the model. Then random noise is added in the image via MATLAB programming. Many methods can restore the noisy and motion blurred image: particularly in this paper inverse filtering as well as wiener filtering are implemented for the restoration purpose consequently, both motion blurred and noisy motion blurred image are restored via inverse filtering as well as wiener filtering techniques and the comparison is made among them.

NLH: A Blind Pixel-Level Non-Local Method for Real-World Image Denoising

Non-local self similarity (NSS) is a powerful prior of natural images for image denoising. Most of existing denoising methods employ similar patches, which is a patch-level NSS prior. In this paper, we take one step forward by introducing a pixel-level NSS prior, i.e., searching similar pixels across a non-local region. This is motivated by the fact that finding closely similar pixels is more feasible than similar patches in natural images, which can be used to enhance image denoising performance. With the introduced pixel-level NSS prior, we propose an accurate noise level estimation method, and then develop a blind image denoising method based on the lifting Haar transform and Wiener filtering techniques. Experiments on benchmark datasets demonstrate that, the proposed method achieves much better performance than previous non-deep methods, and is still competitive with existing state-of-the-art deep learning based methods on real-world image denoising. The code is publicly available at https://github.com/njusthyk1972/NLH.

TARDIS. I. A Constrained Reconstruction Approach to Modeling the z ∼ 2.5 Cosmic Web Probed by Lyα Forest Tomography

Abstract Recent Lyα forest tomography measurements of the intergalactic medium (IGM) have revealed a wealth of cosmic structures at high redshift (z ∼ 2.5). In this work, we present the Tomographic Absorption Reconstruction and Density Inference Scheme (TARDIS), a new chronocosmographic analysis tool for understanding the formation and evolution of these observed structures. We use maximum likelihood techniques with a fast nonlinear gravitational model to reconstruct the initial density field of the observed regions. We find that TARDIS allows accurate reconstruction of smaller-scale structures than standard Wiener-filtering techniques. Applying this technique to mock Lyα forest data sets that simulate ongoing and future surveys such as CLAMATO, Subaru PFS, or the ELTs, we are able to infer the underlying matter density field at observed redshift and classify the cosmic web structures. We find good agreement with the underlying truth in both the characteristic eigenvalues and eigenvectors of the pseudo-deformation tensor, with the eigenvalues inferred from 30 m class telescopes correlated at r = 0.95 relative to the truth. As an output of this method, we are able to further evolve the inferred structures to late time (z = 0) and also track the trajectories of coeval z = 2.5 galaxies to their z = 0 cosmic web environments.