Search

Medical image processing generally contains high components of noise produced by interference, compression and use of imperfect instrument during acquisition or transmission. An effective imaging devicei.e. Magnetic Resonance Imagingmay diagnose the disease by acute analysis of dissectional anatomical soft tisses of human. In general, MR images are of poor contrast in lieu of blurriness, out of focused and lack of brightness iside the machine. In this paper, hybridization approaches i.e. Median-Wiener filter (MW), Wiener-Median filter (WM) and other combinations like WMWM & MWMW are proposed for MR image enhancement. The results are further compared with various filtering algorithms i.e. Average filter, Median Filter, Wiener Filter &Gaussian filter and in terms of MSE, PSNR, RMSE, MAE. The proposed hybridization filtering technique gives better outcomes comparatively. [ABSTRACT FROM AUTHOR]

Speech is a signal produced by humans to interact and communicate. Different information is gained from speech signals, such as the language being spoken, emotion, gender, speaker identification, and other information. Speech signals are exposed to different noises, which can be generated at the beginning of the speech or during the transmission. Due to this problem, noise reduction processes are an interesting field in different communication application systems that cultivate the intelligibility and quality of speech signals. It refers to removing or reducing the background noise in order to obtain an improved quality of the original speech signal without distorting the original (clean) signal. This paper reviews the state-of-the-art research, reviewing different speech enhancement filters and algorithms and comparing their performance to reach a conclusion about which is the best filter or the most effective one based on the kind of noise that was used and the most difficult noise to remove from the signal. [ABSTRACT FROM AUTHOR]

The cancer in lungs is termed as a severe disease that can lead to huge deaths globally. The timely identification can be useful to increase rate of survival. A Computed Tomography is used to identify position of tumor and discover level of cancer. The present study provides an innovative model with CT to identify lung cancer. The lung CT is used for analyzing the cancer levels. These images undergoes pre-processing wiener filter to eradicate the noise. Subsequently, the lung lobe segmentation is attained with LadderNet whose training is executed with Archimedes Flow Regime Optimization (AFRO). The detection of nodule is performed using grid based strategy followed by extraction of statistical, GLCM and texture features. Finally, lung cancer detection are performed using DBN and the hyper parameters of DBN are optimally fine tuned using devised AFRO. The AFRO-based DBN provided finest efficiency with accuracy of 95.8%, F-measure of 92.6% and precision of 93.6%.The finest outcomes revealed that the technique has feasible robustness in classifying the nodules. The technique poses the ability to help the radiologists to interpret the data more effective and distinguish the lung nodules with CT images. [ABSTRACT FROM AUTHOR]

In many areas, such as speech processing, communication, and measurement systems, where the actual signal is not directly available, Wiener deconvolution is a widely used signal restoration technique. The accuracy of the estimated signal in Wiener deconvolution is determined by the unknown filter parameter. To compute this unknown parameter, complex and time-consuming numerical as well as iterative computation approaches were proposed in the literature, which also often compute an unoptimized unknown filter parameter. To address the computational problem and estimate a more accurate unknown filter parameter, the authors reported an analytical solution in a prior work for a given signal to noise ratio. However, it was revealed that the actual optimum parameter value also varies with noise also. In this paper, the authors propose a new analytical solution for optimum parameter estimation that includes noise as well. The authors compared the modified analytical solution to existing numerical computation methods for different values of Signal to Noise Ratio and demonstrated that the proposed methods' optimum parameter estimation is always close to the actual optimum value, which resulted in more accurate signal deconvolution. Proposed mathematical solutions are also verified with experimental signals, acquired in a laboratory. [ABSTRACT FROM AUTHOR]

Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rheumatoid arthritis (RA) leads to the destruction, deformation, and loss of function and causes joint damages to 85% of patients. The detection of bone mineral density from traditional x‐ray images consumes more time and it is observer dependent which decreases the evaluation performance when RA is in its early stage. Therefore, it is necessary to develop an observer‐independent computer‐aided automatic analysis system for evaluating JS narrowing. An efficient RA detection system based on feed‐forward neural network is proposed in this article. Initially the dataset is preprocessed to remove blur, redundant data and nonlinearities in RA images using wiener filter in less computation time. The edge boundaries are detected using nonlinear partial differential equation as the texture features of bones has great impact on the system accuracy. The detection strategy is implemented by optimized gray‐level co‐occurrence matrix based on bone image features. The selected features are optimized by genetic algorithm to improve the classification performance. The images are then classified as inflamed and noninflamed by radial basis function neural network. The effectiveness of the proposed classification approach is verified in terms of accuracy, sensitivity, and specificity and compared with the conventional convolutional neural network, artificial neural network, and support vector machine classifiers. Among the several existing techniques, our method found to be much effective with a maximum accuracy of 98.5% as some novel approaches are proposed and also tends to possess more compatible than the present system. Our computerized rheumatoid arteries detection approach will give more precise and flawless consistency rate. [ABSTRACT FROM AUTHOR]

Protecting maritime borders is crucial to ensuring overall border security. Law enforcement agencies make great use of analyzing images of underwater debris to gather intelligence and detect illicit materials. Underwater image improvement contributes to better data quality and analytical. Nevertheless, underwater image analysis poses greater challenges compared to analyzing images taken above the water, factors like refraction of light and darkness contribute to the degradation of underwater image quality. In this paper, a novel approach is proposed to enhance underwater images, the proposed approach involves splitting underwater colored image to its three basic components, Subsequently, a point spread function is created for each component to describes image blurring factor, The deblurring process is then applied by using wiener filter, the result sharped by sharping filter to clarify edges, contrast linear stretch is performed to improve contrast and visual details. and the resulting image is finally reassembled from the three basic components. The proposed method showed effective results in evaluating the main metrics and gave better results when compared to a number of different methods. These results prove the effectiveness of the proposed method and its ability to practical applications in improving image quality. [ABSTRACT FROM AUTHOR]

In linear system identification problems, the Wiener filter represents a popular tool and stands as an important benchmark. Nevertheless, it faces significant challenges when identifying long-length impulse responses. In order to address the related shortcomings, the solution presented in this paper is based on a third-order tensor decomposition technique, while the resulting sets of Wiener–Hopf equations are solved with the conjugate gradient (CG) method. Due to the decomposition-based approach, the number of coefficients (i.e., the parameter space of the filter) is greatly reduced, which results in operating with smaller data structures within the algorithm. As a result, improved robustness and accuracy can be achieved, especially in harsh scenarios (e.g., limited/incomplete sets of data and/or noisy conditions). Besides, the CG-based solution avoids matrix inversion operations, together with the related numerical and complexity issues. The simulation results are obtained in a network echo cancellation scenario and support the performance gain. In this context, the proposed iterative Wiener filter outperforms the conventional benchmark and also some previously developed counterparts that use matrix inversion or second-order tensor decompositions. [ABSTRACT FROM AUTHOR]

In order to avoid cancer, it is imperative that microcalcification in the breast be found. It is sufficiently small to be difficult to discern with the unassisted eye. Computer-based detection output is modest and tends to stay concealed from the radiologist doing the examination, which might help the radiologist increase diagnostic accuracy. According to this study, the best Wiener Linear Time Invariant Filter method with Tophat Transformation (LFWT) can identify microcalcification in the breast with an accuracy rate of 99.5%. In this work, we focused on the identification of microcalcifications in images, an essential initial step towards precisely identifying all the indicators in a mammography-based early breast cancer diagnosis. To make the cancer region visible and prominent, the Wiener and CLAHE filters are used. Tophat morphological operators were applied to mask detection, and edges were extracted. The analytical performance of the proposed model for microcalcification identification in mammograms was evaluated and compared with other approaches using Mammographic Image Analysis Society (MIAS) and Mini-Mammographic imaging datasets. Additionally, three techniques- The Local Contrast Method (LCM), the Local Relative Contrast Measure Method (LRCMM), and the High-Boost-Based Multiscale Local Contrast Measure (HBBMLCM) are used to identify microcalcification linked to cancer on mammography images. Performance Evaluation of the Proposed Model: the LFWT methodology had the best level of efficacy in detecting microcalcification linked to breast cancer. The suggested LFWT technique finds each and every tiny point on the MIAS dataset’s mammography.

Recently, consumer product recognition comprises harnessing cutting-edge technologies namely artificial intelligence (AI) and computer vision (CV) to develop the purchasing expedition. This technology allows retailers to utilize robust product recognition systems that precisely identify and categorize products in real time. The comprehension of automatic product identification becomes of major importance for both social and economic improvement since it is more reliable and time-consuming than manual function. Product detection through images is a complex task in the domain of CV. This can be obtained the improving consideration because of the excellent application viewpoint like visually impaired assistance, stock tracking, automatic checkout, and planogram compliance. Currently, deep learning (DL) prefers a successful progression with great achievements in object detection and image classification. Therefore, this study presents Advanced Consumer Product Recognition using the Aquila Optimization Algorithm with Deep Learning (ACPR-AOADL) technique. The proposed ACPR-AOADL model utilizes hyperparameter-tuned DL concepts for the identification of consumer products. To achieve this, the ACPR-AOADL model first pre-processes the input data utilizing a Wiener filter (WF) to improve the image quality. Besides, the YOLO-v8 model with a deep residual network (DRN) as a backbone network can be applied for the product detection process. For product classification, the deep belief network (DBN) approach can be used. To boost the complete product detection process, the ACPR-AOADL technique involves AOA based hyperparameter selection process. The performance analysis of the ACPR-AOADL method can be examined under the Product-10K dataset. Wide-ranging results stated that the ACPR-AOADL technique reaches enhanced classification performance over other compared approaches.

Medical imaging plays an important role in modern healthcare, with Computed Tomography (CT) being essential for high-resolution cross-sectional imaging. However, Gaussian noise often occurs within the CT scan images and makes it difficult for image interpretation and reduces the diagnostic accuracy, creating a significant obstacle to fully utilizing CT scanning technology. Existing denoising techniques have a hard time balance between noise reduction and preserving the important image details, failing to enable the optimal diagnostic precision. This study introduces Adaptive Gaussian Wiener Filter (AGWF), a novel filter aims to denoise CT scan images that have been corrupted with various Gaussian noise variance without compromising the image details. The AGWF combines the Gaussian filter for initial noise reduction, followed by the implementation of Wiener filter, which can adaptively estimate noise variance and signal power in localized regions. This approach not only outperforms other existing techniques but also showcases a remarkable balance between noise reduction and image detail preservation. The experiment evaluates 300 images from the dataset and each image is corrupted with Gaussian noise variance to ensure a comprehensive evaluation of the AGWF's performance. The evaluation indicated that AGWF can improve the Signal-to-Noise Ratio (SNR) value and reduce the Root Mean Square Error (RMSE) and Mean Square Error (MSE) value, showing a qualitative improvement in CT scan imagery. The proposed method holds promising potential for advancing medical imaging technology with the implementation of deep learning. [ABSTRACT FROM AUTHOR]

In day-to-day life, images are the most frequent and casual way of information sharing. These images are susceptible to external disturbances or noise. Thus, to curb noise, image denoising algorithms are utilized. In this paper, the variational mode decomposition, with its concurrent and a non-recursive process for determining the mode functions that also provides a robust method for image denoising, has been introduced. This decomposition process divides the whole spectrum of the signal into a number of sub-bands or mode functions, centered around their respective center frequencies. To these mode functions, spatial filters such as bilateral filter, wiener filter, and modified anisotropic diffusion filter are employed. These filters help in enhancing the yield of the quality assessment metrics; such as mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), together with the semi-adaptive conductance function in the diffusion filter. The parameters of these respective spatial filters are calibrated, and then selected in order to get the best possible metric scores. The applicability and ability of the algorithm to suppress noise are compared visually and quantitatively for the noisy image using modified variational mode decomposition and other denoising algorithms in both low and high noise levels. The algorithm provides an average decrease of 62% in case of MSE, 28% increase in PSNR, and 110% increase in SSIM when compared with other denoising techniques. The estimated metric score values signify that the proposed method has a better prospect as a denoising algorithm. [ABSTRACT FROM AUTHOR]

The article analyzes modern methods of optical coherence tomography (OCT) for the diagnosis of ophthalmic diseases. The features of methods and schemes of optical coherence tomography are analyzed. A comparative analysis of medical image filtering algorithms and their features in the context of using OCT for image filtering is carried out. [ABSTRACT FROM AUTHOR]

Objective: Oticon Medical cochlear implant (CI) systems were evaluated for noise reduction effectiveness using voice track (VT) and adaptive directional microphones to determine the optimum CI fitting parameters, especially in noisy environments. Methods: Ten CI users (3 females, mean age 28.22; 7 males, mean age 36.71) participated in the study. Speech recognition in noise was evaluated in different test conditions with 2-syllable word lists in stationary speech-shaped noise. Five different test conditions were determined for each participant using a combination of software-based VT and directional microphone technology. In the first condition, the omnidirectional microphone (M) was selected and VT was turned off. The fully directional M and strong VT combination were activated in the second condition. In the third condition, the omnidirectional M and strong VT were set. In the fourth condition, automatic adaptive directional M and medium VT were used. Finally, omnidirectional M and medium VT were selected. Results: Speech comprehension scores obtained in a quiet environment were significantly better than those of the other 4 conditions in a noisy environment (P < .01). In addition, the best noise comprehension score (44%) was obtained in the fourth condition, although this was not statistically significant (P < .05). Conclusion: The VT technology was more effective in understanding speech in noise when used in the medium setting and with an automatic adaptive directional microphone. [ABSTRACT FROM AUTHOR]

Computer vision algorithms are widely used in solving a number of applied problems. The correct operation of such algorithms depends on the photo and video data that they receive at the input, which are subject to the effect of noise; hence, noise suppression is an important stage in low-level digital image processing. In this work, the Wiener filtering of normal white noise with using neural networks in the domain of the discrete wavelet transform is studied. The architecture of the networks and the algorithm developed for their application for filtering in the domain of a discrete wavelet transform are described. The proposed algorithm is tested on the BSDS500 dataset at various noise levels. The filtering quality is evaluated by the calculated signal-to-noise ratio (SNR) and structural similarity index (SSIM) values. The results of processing test images indicate that the developed algorithm is superior in noise reduction quality to most of the other considered filters, including Wiener filtering without the use of neural networks in the domain of the discrete wavelet transform. [ABSTRACT FROM AUTHOR]

Many works in the state of the art are interested in the increase of the camera depth of field (DoF) via the joint optimization of an optical component (typically a phase mask) and a digital processing step with an infinite deconvolution support or a neural network. This can be used either to see sharp objects from a greater distance or to reduce manufacturing costs due to tolerance regarding the sensor position. Here, we study the case of an embedded processing with only one convolution with a finite kernel size. The finite impulse response (FIR) filter coefficients are learned or computed based on a Wiener filter paradigm. It involves an optical model typical of codesigned systems for DoF extension and a scene power spectral density, which is either learned or modeled. We compare different FIR filters and present a method for dimensioning their sizes prior to a joint optimization. We also show that, among the filters compared, the learning approach enables an easy adaptation to a database, but the other approaches are equally robust. [ABSTRACT FROM AUTHOR]

At present, to settle the question of excessive noise in the speech signal during the call of mobile devices in China, the research proposes that the Wiener filter and the generative adversarial network are combined into the IGAN algorithm. Firstly, the Wiener filter regularization algorithm is introduced to construct the preprocessing model of the speech signal; then the preprocessing model is fused with the generative adversarial network algorithm to construct the denoising model. Finally, the performance analysis and simulation experiments of the application effect of the model are carried out. The results show that in the experiment comparing IGAN with five traditional algorithms, when the SNR ratio is increased to 17.5 dB, the MOS and PESQ scores under the IGAN method can reach 4.9 and 3.5 respectively, and the DNN effect is second only to IGAN. Other algorithms perform poorly. Then compare the number of iterations and the loss value between the two. When the network voice signal begins to converge, the loss value corresponding to DNN is 1.132; while the loss value of IGAN is about 0.573, it can be found that the loss value of IGAN has dropped by half, which shows that IGAN Build the model with a smaller loss value. And IGAN tends to converge when iteratively is performed for about 200 times, and the average peak SNR can reach up to 33.85 dB, an increase of nearly 1.02 dB, and the effect is remarkable. This all shows that the IGAN algorithm has the best denoising performance for network speech signals, improves the denoising efficiency, and is conducive to obtaining a denoising signal with a higher fit with the clean signal, so that mobile devices can better serve the people. [ABSTRACT FROM AUTHOR]

In linear system identification problems, the Wiener filter represents a popular tool and stands as an important benchmark. Nevertheless, it faces significant challenges when identifying long-length impulse responses. In order to address the related shortcomings, the solution presented in this paper is based on a third-order tensor decomposition technique, while the resulting sets of Wiener–Hopf equations are solved with the conjugate gradient (CG) method. Due to the decomposition-based approach, the number of coefficients (i.e., the parameter space of the filter) is greatly reduced, which results in operating with smaller data structures within the algorithm. As a result, improved robustness and accuracy can be achieved, especially in harsh scenarios (e.g., limited/incomplete sets of data and/or noisy conditions). Besides, the CG-based solution avoids matrix inversion operations, together with the related numerical and complexity issues. The simulation results are obtained in a network echo cancellation scenario and support the performance gain. In this context, the proposed iterative Wiener filter outperforms the conventional benchmark and also some previously developed counterparts that use matrix inversion or second-order tensor decompositions.

Coherently Distributed (CD) sources are suitable for practical direction-of-arrival (DOA) estimation in massive Multiple-Input Multiple-Output (MIMO) communication systems. However, massive MIMO systems employ a large number of antennas to improve performance, which significantly raises the complexity of conventional DOA estimation algorithms for CD sources. To reduce the complexity, a novel low-complexity CD unitary estimation of signal parameters via the rotational invariance technique (U-ESPRIT) algorithm based on discrete cosine transformation (DCT) beamspace transformation and multistage Wiener filter (BU-ESPRIT) is proposed in this paper. To concentrate the signal energy and make better use of beamspace features, the DCT transformation is used to extract the signal. For the sake of reducing the complexity and achieving accurate angle estimation, U-ESPRIT is performed in beamspace. Moreover, the covariance matrix matching process and eigenvalue decomposition of the covariance matrix are substituted by forward recursion of the multistage Wiener filter to further reduce the complexity of the proposed algorithm. Theoretical analysis indicates the computational superiority of the proposed algorithm over that of some existing algorithms. Simulation results demonstrate that the proposed algorithm can be utilized to efficiently estimate angles of CD sources in massive MIMO systems. [ABSTRACT FROM AUTHOR]

The Wiener filter is widely used in image de-noising. It is used to reduce Gaussian noise. Although the Wiener filter removes noise from the image, it causes a loss of edge detail information, resulting in blurring of the image. The edge details are considered high-frequency components. The Wiener filter is unable to reconstruct these components. In this paper, the proposed filter based on the Wiener filter and the high-boost filter for medical images is presented. The proposed filter is applied to the degraded image. First, using Fourier Transformation, the degraded image and the high boost filter are converted in the frequency domain. Secondly, the wiener filter is applied to the image along with the high boost filter. Thirdly, the deconvolution process is achieved on the image with the high boost filter. Finally, to reconstruct the sharper image in the spatial domain, the inverse of the Fourier transformation is applied. The proposed filter works to suppress the additive noise at the same time. It can keep the image's edge details. Some focus operators are used, which are image contrast, gradient energy, histogram entropy, and spatial frequency, in order to test the proposed algorithm. Experimental results showed that the proposed filter gives good results compared with the traditional filters for medical images, especially dark images. [ABSTRACT FROM AUTHOR]

The traditional ultrasonic dynamic focusing imaging which simply uses the delay-and-sum method in beamforming shows low resolution and poor contrast. In this study, based on the coherent pixel-based beamforming (Coherent PB), a novel Wiener pre-filter with high computational efficiency is applied to correct the phase of the entire array signal by using the pulse-echo at the focal point. We also apply a Wiener post-filter calculating adaptive weights for the beamforming results of each sub-aperture to suppress the noise and artifacts. The proposed method's effectiveness is verified through simulation experiments, phantom experiments, and in vivo experiments. Compared with Coherent PB, the proposed method significantly improves the axial resolution and contrast of images while maintaining the high lateral resolution and computational efficiency, which shows certain clinical application value.

Parkinson's disease is a progressive neurodegenerative disorder caused by dopaminergic neuron degeneration. Parkinsonian speech impairment is one of the earliest presentations of the disease and, along with tremor, is suitable for pre-diagnosis. It is defined by hypokinetic dysarthria and accounts for respiratory, phonatory, articulatory, and prosodic manifestations. The topic of this article targets artificial-intelligence-based identification of Parkinson's disease from continuous speech recorded in a noisy environment. The novelty of this work is twofold. First, the proposed assessment workflow performed speech analysis on samples of continuous speech. Second, we analyzed and quantified Wiener filter applicability for speech denoising in the context of Parkinsonian speech identification. We argue that the Parkinsonian features of loudness, intonation, phonation, prosody, and articulation are contained in the speech, speech energy, and Mel spectrograms. Thus, the proposed workflow follows a feature-based speech assessment to determine the feature variation ranges, followed by speech classification using convolutional neural networks. We report the best classification accuracies of 96% on speech energy, 93% on speech, and 92% on Mel spectrograms. We conclude that the Wiener filter improves both feature-based analysis and convolutional-neural-network-based classification performances. [ABSTRACT FROM AUTHOR]

Brain magnetic resonance images (MRI) are used to diagnose the different diseases of the brain, such as swelling and tumor detection. The quality of the brain MR images is degraded by different noises, usually salt & pepper and Gaussian noises, which are added to the MR images during the acquisition process. In the presence of these noises, medical experts are facing problems in diagnosing diseases from noisy brain MR images. Therefore, we have proposed a de-noising method by mixing concatenation, and residual deep learning techniques called the MCR de-noising method. Our proposed MCR method is to eliminate salt & pepper and gaussian noises as much as possible from the brain MRI images. The MCR method has been trained and tested on the noise quantity levels 2% to 20% for both salt & pepper and gaussian noise. The experiments have been done on publically available brain MRI image datasets, which can easily be accessible in the experiments and result section. The Structure Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) calculate the similarity score between the denoised images by the proposed MCR method and the original clean images. Also, the Mean Squared Error (MSE) measures the error or difference between generated denoised and the original images. The proposed MCR denoising method has a 0.9763 SSIM score, 84.3182 PSNR, and 0.0004 MSE for salt & pepper noise; similarly, 0.7402 SSIM score, 72.7601 PSNR, and 0.0041 MSE for Gaussian noise at the highest level of 20% noise. In the end, we have compared the MCR method with the state-of-the-art de-noising filters such as median and wiener de-noising filters. [ABSTRACT FROM AUTHOR]

The economic sustainability of aquifers across the world relies on accurate and rapid estimates of groundwater storage changes, but this becomes difficult due to the absence of in-situ groundwater surveys in most areas. By closing the water balance, hydrologic remote sensing measures offer a possible method for quantifying changes in groundwater storage. However, it is uncertain to what extent remote sensing data can provide an accurate assessment of these changes. Therefore, a new framework is implemented in this work for predicting the underground water level using remote sensing images. Generally, the water level is defined into five levels: Critical, Overexploited, Safe, Saline, and Semi-critical, based on water quantity. In this manuscript, the remote sensing images were acquired from remote sensing images. At first, Wiener filtering was employed for preprocessing. Secondly, the Vegetation Indexes (VI) (Normalized Difference Vegetation Index (NDVI), Normalized Difference Snow Index (NDSI), Infrared index (IRI), Radar Vegetation Index (RVI)), and statistical features (entropy, Root Mean Square (RMS), Skewness, and Kurtosis) were extracted from the preprocessed remote sensing images. Then, the extracted features were combined as a novel hydro index, which was fed to the Ensemble Classifier (EC): Neural Networks (NN), Support Vector Machine (SVM), and improved Deep Convolutional Neural Network (DCNN) models for underground water level prediction in the remote sensing images. The obtained results prove the efficacy of the proposed framework by using different performance measures. The results shows that the False Positive Rate (FPR) of the proposed EC model is 0.0083, which is better than that of existing methods. On the other hand, the proposed EC model has a high accuracy of 0.90, which is superior to the existing traditional models: Long Short-Term Memory (LSTM) network, Naïve Bayes (NB), Random Forest (RF), Recurrent Neural Network (RNN), and Bidirectional Gated Recurrent Unit (Bi-GRU). [ABSTRACT FROM AUTHOR]

An effective instrument for obtaining an image of the retina is an optical coherence tomography (OCT) imaging device. OCT images of the retina are useful for diagnosing and tracking eye diseases. However, different physical configurations in the imaging apparatus are to blame for the speckle noise in retinal OCT images. The OCT image quality and assessment reliability are reduced due to aforementioned noise. This paper offered a paradigm for reducing speckle noise that was motivated by the mathematical formulation of speckle noise. Two distinct noise components make up speckle noise, one of which is additive and the other of which is multiplicative in nature. For each sort of noise, the suggested structure employs a different filter. To reduce the additive component of speckle noise, Weiner filtering is used. To minimize the multiplicative component of noise, a particular arrangement based on non-subsampled shearlet transform (NSST) is used. It is now widely acknowledge that NSST overcome the limitations of traditional wavelet transform therefore it very useful in dealing of distributed discontinuities therefore it is prefer in this research work .Real retinal OCT pictures are used to assess the proposed framework's quantitative and qualitative performance. The PSNR, MSE, SSIM, and CNR metrics are used to compare the suggested framework. In comparison to existing cutting-edge filters, the proposed framework performs better in terms of noise suppression capability with structure preservation capabilities. The proposed technique gives highest PSNR, SSIM and CNR value that indicate the effectiveness of proposed work in addition to this proposed work give lowest MSE value. The proposed work give better enhance images in comparison to other existing filter therefore it may be helpful to find out any abnormality in OCT image and improve the diagnose of OCT retinal image. [ABSTRACT FROM AUTHOR]

Agriculture is one of India's greatest money makers and a measure of financial growth. Rice is one of India's most widely grown crops as a staple diet. Rice crops have been shown to be heavily afflicted by illnesses, resulting in significant losses in agriculture. Rice leaf diseases not only cause a loss of revenue for farmers, but they also decrease the quality of their final output. External appearances of diseased rice leaves can be subjected to image processing processes. On the other hand, disease sickness may vary depending on the different leaves. Each disease has its own distinct features, some of leaves have the same colour but various shapes, while others have different colours but the same shapes. Farmers are sometimes confused and unable to make an accurate judgement when it comes to pesticide choosing. To solve this problem, a hybrid CNN (Inception-ResNet)-SVM model for detecting and treating damaged rice leaves has been developed. In this designed model, the images are collected and gathered by capturing rice leaf using camera at the agricultural field. These images are refined to improve image quality and visibility for reliable estimation, and then segregated using Grab-Cut algorithm to eliminate undesired sections of image. Features of the segmented images are extracted and classified using hybrid CNN (Inception-Resnet V2)-SVM algorithm. The developed model's study results are analysed and discussed to recent techniques. The suggested model achieved accuracy, precision, recall, and error values of 0.97, 0.93 and 0.03 accordingly. As a conclusion, suggested model outperforms revious methodologies. [ABSTRACT FROM AUTHOR]

The earthquake waveform distortion and phase shifting are the main issues of a frequency-based filter application. A combined time–frequency domain filter technique needs to be considered as a solution for maximizing earthquake signal enhancement with the waveform still preservable. We compare the performance assessment result of the Butterworth filter, a popular frequency-based filter, and the Wiener filter, a time–frequency filter technique. This study analyzes the ability to reduce the background noise by using SNR (signal-to-noise ratio) change calculation and maintaining earthquake waveform by using waveform cross-correlation of the clean signal reference and filtered signal. We assessed both algorithms using synthetic and observed seismic data. For the synthetic test, the clean signal reference is added by various noise types and levels. The observed seismic data used are 330 datasets of the vertical component seismogram from 100 earthquake events recorded at four seismic stations in North Sulawesi, Indonesia, including MGAI, PSSI, SGSI, and TMSI stations. Generally, the Wiener filter technique has a better performance than the Butterworth filter. Wiener filter can improve SNR better than the Butterworth filter in the synthetic and real-data tests. The Wiener filter also favorably maintains the waveform with a range of normalization cross-correlation between 0.67 and 0.9. In contrast, the Butterworth filter typically deforms the original signal shape, as can be seen from the low normalization cross-correlation value. [ABSTRACT FROM AUTHOR]

Speech enhancement is the process of enhancing the clarity and intelligibility of speech signals that have been degraded due to background noise. With the assistance of deep learning, a novel speech signal enhancement model is introduced in this research. The proposed model is divided into two phases: (i) Training (ii) Testing. In the training phase, the noise spectrum and signal spectrum are estimated via a Non-negative Matrix Factorization (NMF) from the noisy input signal. Then, Empirical Mean Decomposition (EMD) features are extracted from the Wiener filter. The de-noised signal is acquired from EMD, the bark frequency is evaluated and the Fractional Delta AMS features are extracted. The key contribution of this study is the use of the Long Short Term Memory (LSTM) model to properly estimate the tuning factor η of the Wiener filter for all input signals. The LSTM was trained by the extracted features (EMD) via a modified wiener filter for decomposing the spectral signal and the output of EMD is the denoised enhanced speech signal. A comparative evaluation is carried out between the proposed and existing models in terms of error measures. [ABSTRACT FROM AUTHOR]

In today's scientific epoch, speech is an important means of communication. Speech enhancement is necessary for increasing the quality of speech. However, the presence of noise signals can corrupt speech signals. Thereby, this work intends to propose a new speech enhancement framework that includes (a) training phase and (b) testing phase. The input signal is first given to STFT-based noise estimate and NMF-based spectra estimate during the training phase in order to compute the noise spectra and signal spectra, respectively. The obtained signal spectra and noise spectra are then Wiener-filtered, then empirical mean decomposition (EMD) is used. Because the tuning factor of Wiener filters is so important, it should be computed for each signal by coaching in a fuzzy wavelet neural network (FW-NN). Subsequently, a bark frequency is computed from the denoised signal, which is then subjected to FW-NN to identify the suitable tuning factor for all input signals in the Wiener filter. For optimal tuning of η , this work deploys the fitness-oriented elephant herding optimization (FO-EHO) algorithm. Additionally, an adaptive Wiener filter is used to supply EMD with the ideal tuning factor from FW-NN, producing an improved speech signal. At last, the presented approach's supremacy is proved with varied metrics. [ABSTRACT FROM AUTHOR]

The quality of images, especially the paintings and other art works often degrades due to various reasons e.g; ageing effect, usage of low quality material for paintings, improper preservation, introduction of different noises etc. The degraded images need proper restoration. Rising need of image quality enhancement in various fields have led to the development of different kinds of filtering and other techniques. Over the years many linear and nonlinear filters have been developed and applied to the poor quality and distorted images to remove the noise and improve the quality. This paper deals with the restoration of old and almost destroyed art works. Four different types of filters namely Median Filter, Gaussian Filter, Wiener Filter and Average (or Mean) Filter are used for denoising purpose. Later on Contrast Limited Adaptive Histogram Equalization is also used for further improvement of image quality. Various parameters like AMBE, PSNR, NMSE, CPP and EME have been calculated to measure the performance of the combination of different filters and CLAHE technique. MATLAB software is used for the implementation of the above mentioned analysis. [ABSTRACT FROM AUTHOR]

Image restoration is the process of estimating the original image content from a degraded picture. In this paper, the Richardson-Lucy iterative algorithm was developed to improve the quality of degraded medical images. It has been assumed that medical images are exposed to two types of degradation. The first type is the blur function in the Gaussian form with different widths, i.e. σ = 1, 2, and 3. The second type of degradation was assumed to be of the independent white Gaussian noise type with different signal-to-noise ratio values: SNR = 10, 50, and 100. The results obtained from the adaptive filter are compared, quantitatively, with different conventional filters: inverse, Wiener, and constraint least square, by applying different measures, such as: power signal to noise ratio (PSNR), structural similarity index (SSID), and root mean square error (RMSE). The comparison showed that the adaptive recovery filter achieves better results. [ABSTRACT FROM AUTHOR]