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1
Biradar, Nagashettappa, M. L. Dewal, and Manoj Kumar Rohit. "Edge Preserved Speckle Noise Reduction Using Integrated Fuzzy Filters." International Scholarly Research Notices 2014 (October 30, 2014): 1–11. http://dx.doi.org/10.1155/2014/876434.
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Echocardiographic images are inherent with speckle noise which makes visual reading and analysis quite difficult. The multiplicative speckle noise masks finer details, necessary for diagnosis of abnormalities. A novel speckle reduction technique based on integration of geometric, wiener, and fuzzy filters is proposed and analyzed in this paper. The denoising applications of fuzzy filters are studied and analyzed along with 26 denoising techniques. It is observed that geometric filter retains noise and, to address this issue, wiener filter is embedded into the geometric filter during iteration process. The performance of geometric-wiener filter is further enhanced using fuzzy filters and the proposed despeckling techniques are called integrated fuzzy filters. Fuzzy filters based on moving average and median value are employed in the integrated fuzzy filters. The performances of integrated fuzzy filters are tested on echocardiographic images and synthetic images in terms of image quality metrics. It is observed that the performance parameters are highest in case of integrated fuzzy filters in comparison to fuzzy and geometric-fuzzy filters. The clinical validation reveals that the output images obtained using geometric-wiener, integrated fuzzy, nonlocal means, and details preserving anisotropic diffusion filters are acceptable. The necessary finer details are retained in the denoised echocardiographic images.
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Jacobsen, Bo Holm. "A case for upward continuation as a standard separation filter for potential‐field maps." GEOPHYSICS 52, no. 8 (August 1987): 1138–48. http://dx.doi.org/10.1190/1.1442378.
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Separation filtering is incomplete even under the ideal synthetic condition of known power spectra of the regional and residual fields. I have designed some Wiener filters, which minimize the inevitable separation error, from previous statistical source models of Naidu, and Spector and Grant. This formulation includes the classic separation filters of Strakhov and of Elkins as Wiener filters. A proposed generalization of Wiener filters, denoted as uniformly suboptimum filters, quantitatively supports the statement that a wide span of separation problems may be solved adequately using some convenient, small standard filter family. A uniform random‐source model without assumed vertical correlations invokes upward continuation filters. In addition to this role as a Wiener filter, the upward continuation operator is given by elementary functions in both space and wavenumber domains, is numerically stable, and is also physically comprehensible when applied to real, nonrandom anomalies. In view of these distinguishing features, I propose to use the upward continuation operator to build a convenient, standard family of separation filters.
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Linville, A. Frank. "Single‐channel digital filter design for seismic applications." GEOPHYSICS 59, no. 10 (October 1994): 1584–92. http://dx.doi.org/10.1190/1.1443547.
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A general least‐squares, time‐domain filter design methodology has been developed that is easy to use for a variety of seismic filtering applications. The 1-D finite‐impulse response frequency filter can efficiently provide the noise attenuation and selectivity needed in modern data processing. Flexibility of design allows a choice of all basic types of single‐channel filters commonly used in processing. These include low‐pass, high‐pass, band‐pass, band‐reject, and notch filters. In addition, multiple bands may be passed or rejected using a single operator design without increasing the length of the filter. The ability to reject multiple noise bands with one filter is convenient and also reduces data processing costs. The filter can be viewed as a minimum‐phase Wiener‐Levinson predictive deconvolution filter designed to reject specified frequency bands. The filter is designed from an exact mathematical description of the specified stop bands that provide an explicit expression for the required autocorrelation lags in the normal equations. The filter’s desired frequency response (transition zone width and rejection level) is simply related to two input parameters—operator length and white noise level.
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Dávalos, Antonio, Meryem Jabloun, Philippe Ravier, and Olivier Buttelli. "The Impact of Linear Filter Preprocessing in the Interpretation of Permutation Entropy." Entropy 23, no. 7 (June 22, 2021): 787. http://dx.doi.org/10.3390/e23070787.
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Permutation Entropy (PE) is a powerful tool for measuring the amount of information contained within a time series. However, this technique is rarely applied directly on raw signals. Instead, a preprocessing step, such as linear filtering, is applied in order to remove noise or to isolate specific frequency bands. In the current work, we aimed at outlining the effect of linear filter preprocessing in the final PE values. By means of the Wiener–Khinchin theorem, we theoretically characterize the linear filter’s intrinsic PE and separated its contribution from the signal’s ordinal information. We tested these results by means of simulated signals, subject to a variety of linear filters such as the moving average, Butterworth, and Chebyshev type I. The PE results from simulations closely resembled our predicted results for all tested filters, which validated our theoretical propositions. More importantly, when we applied linear filters to signals with inner correlations, we were able to theoretically decouple the signal-specific contribution from that induced by the linear filter. Therefore, by providing a proper framework of PE linear filter characterization, we improved the PE interpretation by identifying possible artifact information introduced by the preprocessing steps.
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Porsani, Milton J. "Fast algorithms to design discrete Wiener filters in lag and length coordinates." GEOPHYSICS 61, no. 3 (May 1996): 882–90. http://dx.doi.org/10.1190/1.1444013.
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Two recursive algorithms for designing discrete Wiener filters for wavelets of different phase characteristics are the Simpson and the Manolakis recursions. Both procedures are efficient; however, both recursions work with a prefixed length filter. Two fast algorithms to design discrete Wiener filters in lag and length coordinates are presented. The recursion methods of Levinson and Manolakis are combined to generate two fast algorithms that calculate the value for the minimized total squared error (MTSE) corresponding to spiking and shaping filters. For a spiking filter of length n and a wavelet of m data points, [Formula: see text] operations are required to obtain the [Formula: see text] map of the MTSEs, (one operation is defined here as one multiplication and one addition). For a shaping filter, [Formula: see text] operations are required to obtain the corresponding m × n map. These algorithms may be seen as a Levinson recursion on two variables, length j of the filter and lag k, for the desired signal. Numerical examples for spiking and shaping filters are presented.
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Salehi, Hadi, Javad Vahidi, Thabet Abdeljawad, Aziz Khan, and Seyed Yaser Bozorgi Rad. "A SAR Image Despeckling Method Based on an Extended Adaptive Wiener Filter and Extended Guided Filter." Remote Sensing 12, no. 15 (July 23, 2020): 2371. http://dx.doi.org/10.3390/rs12152371.
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The elimination of multiplicative speckle noise is the main issue in synthetic aperture radar (SAR) images. In this study, a SAR image despeckling filter based on a proposed extended adaptive Wiener filter (EAWF), extended guided filter (EGF), and weighted least squares (WLS) filter is proposed. The proposed EAWF and EGF have been developed from the adaptive Wiener filter (AWF) and guided Filter (GF), respectively. The proposed EAWF can be applied to the SAR image, without the need for logarithmic transformation, considering the fact that the denoising performance of EAWF is better than AWF. The proposed EGF can remove the additive noise and preserve the edges’ information more efficiently than GF. First, the EAWF is applied to the input image. Then, a logarithmic transformation is applied to the resulting EAWF image in order to convert multiplicative noise into additive noise. Next, EGF is employed to remove the additive noise and preserve edge information. In order to remove unwanted spots on the image that is filtered by EGF, it is applied twice with different parameters. Finally, the WLS filter is applied in the homogeneous region. Results show that the proposed algorithm has a better performance in comparison with the other existing filters.
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Jiang, Hai Bo, and Jing Zhi Cai. "Performance Analysis of Several Common Filter." Applied Mechanics and Materials 220-223 (November 2012): 1446–49. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1446.
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Denoising is the initial stage of image processing, in preparation for the subsequent processing of the image. This article describes a field of several denoising used filters include average filter, median filter, Wiener filter, Kalman filter. Combination of diagrams, will describe their filtering principle, at the end of this paper,analysis signal to noise ratio of image and other performance indicators .
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Bazine, Razika, Huayi Wu, and Kamel Boukhechba. "Spectral DWT Multilevel Decomposition with Spatial Filtering Enhancement Preprocessing-Based Approaches for Hyperspectral Imagery Classification." Remote Sensing 11, no. 24 (December 5, 2019): 2906. http://dx.doi.org/10.3390/rs11242906.
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In this paper, spectral–spatial preprocessing using discrete wavelet transform (DWT) multilevel decomposition and spatial filtering is proposed for improving the accuracy of hyperspectral imagery classification. Specifically, spectral DWT multilevel decomposition (SDWT) is performed on the hyperspectral image to separate the approximation coefficients from the detail coefficients. For each level of decomposition, only the detail coefficients are spatially filtered instead of being discarded, as is often adopted by the wavelet-based approaches. Thus, three different spatial filters are explored, including two-dimensional DWT (2D-DWT), adaptive Wiener filter (AWF), and two-dimensional discrete cosine transform (2D-DCT). After the enhancement of the spectral information by performing the spatial filter on the detail coefficients, DWT reconstruction is carried out on both the approximation and the filtered detail coefficients. The final preprocessed image is fed into a linear support vector machine (SVM) classifier. Evaluation results on three widely used real hyperspectral datasets show that the proposed framework using spectral DWT multilevel decomposition with 2D-DCT filter (SDWT-2DCT_SVM) exhibits a significant performance and outperforms many state-of-the-art methods in terms of classification accuracy, even under the constraint of small training sample size, and execution time.
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Salehi, Hadi, Javad Vahidi, and Homayun Motameni. "A Robust Hybrid Filter Based on Evolutionary Intelligence and Fuzzy Evaluation." International Journal of Image and Graphics 18, no. 04 (October 2018): 1850023. http://dx.doi.org/10.1142/s0219467818500237.
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In this paper, a novel denoising method based on wavelet, extended adaptive Wiener filter and the bilateral filter is proposed for digital images. Production of mode is accomplished by the genetic algorithm. The proposed extended adaptive Wiener filter has been developed from the adaptive Wiener filter. First, the genetic algorithm suggest some hybrid models. The attributes of images, including peak signal to noise ratio, signal to noise ratio and image quality assessment are studied. Then, in order to evaluate the model, the values of attributes are sent to the Fuzzy deduction system. Simulations and evaluations mentioned in this paper are accomplished on some standard images such as Lena, boy, fruit, mandrill, Barbara, butterfly, and boat. Next, weaker models are omitted by studying of the various models. Establishment of new generations performs in a form that a generation emendation is carried out, and final model has a more optimum quality compared to each two filters in order to obviate the noise. At the end, the results of this system are studied so that a comprehensive model with the best performance is to be found. Experiments show that the proposed method has better performance than wavelet, bilateral, Butterworth, and some other filters.
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Ramya Ravi, R., R. S. Vinod Kumar, and N. Shanila. "Artifacts Removal in Melanoma Using Various Preprocessing Filters." International Journal of Engineering & Technology 7, no. 3.27 (August 15, 2018): 104. http://dx.doi.org/10.14419/ijet.v7i3.27.17664.
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Preprocessing plays an important role for artifacts removal and quality improvement. In this paper, noise and hair removal techniques for melanoma in dermoscopic images is proposed. Here the performance of four filters for noise removal namely Wiener, Mean, Median, and Gaussian Filters are studied. Of these the performance of Gaussian filter is proved to be best. In addition Morphological Filter is used for hair removal. The noise and hair removal filtering processes help to enhance the quality of the image and thus aid to improve the segmentation results. The performance of the preprocessing filters is compared using quantifying parameters like MSE, PSNR, and SSIM.
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Kim, Seon Man. "Wearable Hearing Device Spectral Enhancement Driven by Non-Negative Sparse Coding-Based Residual Noise Reduction." Sensors 20, no. 20 (October 10, 2020): 5751. http://dx.doi.org/10.3390/s20205751.
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This paper proposes a novel technique to improve a spectral statistical filter for speech enhancement, to be applied in wearable hearing devices such as hearing aids. The proposed method is implemented considering a 32-channel uniform polyphase discrete Fourier transform filter bank, for which the overall algorithm processing delay is 8 ms in accordance with the hearing device requirements. The proposed speech enhancement technique, which exploits the concepts of both non-negative sparse coding (NNSC) and spectral statistical filtering, provides an online unified framework to overcome the problem of residual noise in spectral statistical filters under noisy environments. First, the spectral gain attenuator of the statistical Wiener filter is obtained using the a priori signal-to-noise ratio (SNR) estimated through a decision-directed approach. Next, the spectrum estimated using the Wiener spectral gain attenuator is decomposed by applying the NNSC technique to the target speech and residual noise components. These components are used to develop an NNSC-based Wiener spectral gain attenuator to achieve enhanced speech. The performance of the proposed NNSC–Wiener filter was evaluated through a perceptual evaluation of the speech quality scores under various noise conditions with SNRs ranging from -5 to 20 dB. The results indicated that the proposed NNSC–Wiener filter can outperform the conventional Wiener filter and NNSC-based speech enhancement methods at all SNRs.
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Fursov, V. A. "Constructing a quadratic-exponential FIR-filter with an extended frequency response midrange." Computer Optics 42, no. 2 (July 24, 2018): 297–305. http://dx.doi.org/10.18287/2412-6179-2018-42-2-297-305.
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This article is concerned with synthesizing filter with finite impulse response (FIR-filters) employed to correct radially symmetric distortions such as defocusing. We propose a new parametric class of finite impulse response filters (FIR-filters) based on a model of the one-dimensional radially symmetric frequency response. In the proposed method, the one-dimensional frequency response is composed of quadratic and exponential functions. The two-dimensional impulse response of the filter is constructed by sampling one-dimensional impulse responses for all directions. The development consists in introducing an extended mid-frequency region of the frequency response, thus increasing the contribution of the frequencies to image correction. Examples are given in order to illustrate the possibility of the high-quality distortion correction. In particular, it is shown that the proposed method provides the restoration quality higher than that obtained when using an optimal Wiener filter (taken from OpenCV).
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Carini, Alberto, Stefania Cecchi, and Simone Orcioni. "Robust Room Impulse Response Measurement Using Perfect Periodic Sequences for Wiener Nonlinear Filters." Electronics 9, no. 11 (October 29, 2020): 1793. http://dx.doi.org/10.3390/electronics9111793.
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The paper discusses a measurement approach for the room impulse response (RIR), which is insensitive to the nonlinearities that affect the measurement instruments. The approach employs as measurement signals the perfect periodic sequences for Wiener nonlinear (WN) filters. Perfect periodic sequences (PPSs) are periodic sequences that guarantee the perfect orthogonality of a filter basis functions over a period. The PPSs for WN filters are appealing for RIR measurement, since their sample distribution is almost Gaussian and provides a low excitation to the highest amplitudes. RIR measurement using PPSs for WN filters is studied and its advantages and limitations are discussed. The derivation of PPSs for WN filters suitable for RIR measurement is detailed. Limitations in the identification given by the underestimation of RIR memory, order of nonlinearity, and effect of measurement noise are analysed and estimated. Finally, experimental results, which involve both simulations using signals affected by real nonlinear devices and real RIR measurements in the presence of nonlinearities, compare the proposed approach with the ones that are based on PPSs for Legendre nonlinear filter, maximal length sequences, and exponential sweeps.
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Hashmi, Anas. "Perceptual Evaluation of Speech Quality for Inexpensive Recording Equipment." Acoustics 3, no. 1 (March 10, 2021): 200–211. http://dx.doi.org/10.3390/acoustics3010014.
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This research studies the perceptual evaluation of speech signals using an inexpensive recording device. Different types of noise-reduction and electronic enhancement filters viz. Hamming window, high-pass filter (HPF), Wiener-filter and no-speech activity-cancelling were applied in compliance with the testing conditions such as P.835. In total, 41 volunteers participated in the study for identifying the effects of those filters following a repeatable approach. Performance was assessed in terms of advanced perceptual audio features. This study is believed to be beneficial for both users and device manufacturers as the suggested technique is relatively simple to embed in operational device algorithms or in the master GPU.
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Rudnitskii, A. G., M. A. Rudnytska, L. V. Tkachenko, and E. D. Pechuk. "Application of fuzzy logic in finding the optimal filter in optoacoustics problems." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 1 (2021): 43–54. http://dx.doi.org/10.17721/1812-5409.2021/1.5.
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Denoising is an important step in the early stage of signal preprocessing in optoacoustic applications. The efficiency of such modern noise removal methods as wavelet or curvlet filtering depends significantly on the numerical combinations and forms of wavelet transform parameters, and the multidimensional extension of such filters is rather non-trivial. These issues are serious obstacle for using of these highly effective filters in the tasks of optoacoustic reconstruction, especially in real laboratorial or medical practice. The objective of our study was to find the optimal filter, convenient for use in laboratorian and medical practice, when the types of noise are a priori unknown, and the filter settings should not take much time. In the offered work spatial filters which have only one parameter of adjustment - the size of a window are considered. Three-dimensional extensions of such well-established denoising techniques, as mean filter, median filter, their adaptive variants (Wiener spatial filter and modified median filter), as well as iterative truncated arithmetic mean filter were analyzed. The proposed filters were tested on a test set that contains versions of Shepp-Logan's three-dimensional phantom with mixtures of Gaussian and alpha-stable noise, as well as speckle noise. The identification of the best filter for simultaneous suppression of these types of interference was carried out using the theory of fuzzy sets. In our tests, a modified median filter and an iterative truncated arithmetic mean filter were rated as the best choice when the goal is to minimize aberrations when noise is not known a priory.
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Sriyanto, Sesar Prabu Dwi. "Adaptive seismic noise reduction using Wiener filter." Jurnal Teknologi dan Sistem Komputer 8, no. 1 (October 17, 2019): 12–20. http://dx.doi.org/10.14710/jtsiskom.8.1.2020.12-20.
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Seismic noise disrupts the earthquake observation system due to the frequency and amplitude of seismic noise similar to the earthquake signal. The filter process is one of the methods that can be used to reduce seismic noise. In this study, the Wiener filter algorithm was designed with the Decision-Directed method for Apriori SNR estimation. This filter was chosen because it is adaptive, so it can adjust to environmental conditions without requiring manual parameter settings. The data used are earthquake signals that occur in the Palu area, Central Sulawesi, which are recorded on PKA29 temporary seismic station from February 3 to April 28, 2015. After each signal data has been filtered, then it is evaluated by calculating SNR differences before and after filtering, the signal's dominant frequency, and the cross-correlation of the signal before and after filtering. As a result, the Wiener filter is able to reduce the noise content in earthquake signals according to noisy frequencies before earthquake signals. The impact is that SNR has increased with an average of 8.056 dB. In addition, this filter is also able to maintain the shape of earthquake signals. This is indicated by the normalization value of the cross-correlation between signals before and after the filter which ranges from 0.703 to 1.00.
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Heidrich, G., C. O. Sahlmann, U. Siefker, H. Luig, C. Werner, E. Brunner, J. Meller, and M. Schünemann. "Improvement of tomographic reconstruction in bone SPECT." Nuklearmedizin 45, no. 01 (2006): 35–40. http://dx.doi.org/10.1055/s-0038-1623932.
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Summary Aim: The comparison between iterative reconstruction and filtered backprojection in the reconstruction of bone SPECT in the diagnosis of skeletal metastases. Patients, methods: 47 consecutive patients (vertebral segments: n = 435), with suspected malignancy of the vertebral column, were examined by bone scintigraphy and MRI (maximal interval between the two procedures ± 5 weeks). The SPECT-data were reconstructed with an iterative algorithm (ISA) and with filtered backprojection. We defined semiquantitative criteria in order to assess the quality of the tomograms. Conventional reconstruction was performed both by a Wiener-filter and a low-pass-filter. Iterative reconstruction was performed by the ISA algorithm. The clinical evaluation of the different reconstruction algorithms was performed by MRI as the gold-standard. Results: Sensitivity (%): 87.3 (ISA), 86.4 (low-pass), 79.7 (Wiener); specificity (%): 95.3 (ISA), 95 (low-pass), 85.4 (Wiener). The sensitivity of iterative reconstructed SPECT and low-pass reconstructed SPECT was significantly higher (p <0.05) compared with the sensitivity of SPECT reconstructed by the Wiener-filter. The specificity of iterative reconstruction ISA and low-pass-filter reconstructed SPECT were significantly higher compared with the SPECT data reconstructed by the Wiener-filter. ISA was significantly superior to the Wiener- SPECT relating to all criteria of quality. Iterative reconstruction was significantly superior to the low-pass-SPECT relating to 2 of 3 criteria. In addition the Wiener-SPECT was significantly inferior to the low-pass-SPECT regarding to 2 of 3 criteria. Conclusion: In our series the iterative algorithm ISA was the method of choice in the reconstruction of bone SPECT data. In comparison with conventional algorithms ISA offers a significantly higher quality of the tomograms and yields a high diagnostic accuracy.
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Abd Almisreb, Ali, Nooritawati Md Tahir, Ahmad Farid Abidin, and Norashidah Md Din. "Speech Enhancement based on 2D Gabor Filters for Arabic Phoneme Spoken by Malay Speakers." International Journal of Engineering & Technology 7, no. 4.11 (October 2, 2018): 231. http://dx.doi.org/10.14419/ijet.v7i4.11.20813.
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In this paper, a speech enhancement method using 2D Gabor filter is proposed. The proposed filter is used to enhance Arabic phoneme speech signals that have been recorded under control environment namely indoor room recording. All the phoneme signals are spoken by Malay speakers and considered as non-native Arabic speakers. Firstly, corrupted speech signals by noise must be enhanced before further processing. The effectiveness of the suggested approach is evaluated in compare with Wiener filter. It is proven that the proposed 2D Gabor filters performed appropriately for speech enhancement purpose at different wavelengths.
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Abd Almisreb, Ali, Nooritawati Md Tahir, Ahmad Farid Abidin, and Norashidah Md Din. "Speech Enhancement based on 2D Gabor Filters for Arabic Phoneme Spoken by Malay Speakers." International Journal of Engineering & Technology 7, no. 4.11 (October 2, 2018): 271. http://dx.doi.org/10.14419/ijet.v7i4.11.21391.
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In this paper, a speech enhancement method using 2D Gabor filter is proposed. The proposed filter is used to enhance Arabic phoneme speech signals that have been recorded under control environment namely indoor room recording. All the phoneme signals are spoken by Malay speakers and considered as non-native Arabic speakers. Firstly, corrupted speech signals by noise must be enhanced before further processing. The effectiveness of the suggested approach is evaluated in compare with Wiener filter. It is proven that the proposed 2D Gabor filters performed appropriately for speech enhancement purpose at different wavelengths.
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Kleinherenbrink, Marcel, Riccardo Riva, and Yu Sun. "Sub-basin-scale sea level budgets from satellite altimetry, Argo floats and satellite gravimetry: a case study in the North Atlantic Ocean." Ocean Science 12, no. 6 (November 15, 2016): 1179–203. http://dx.doi.org/10.5194/os-12-1179-2016.
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Abstract. In this study, for the first time, an attempt is made to close the sea level budget on a sub-basin scale in terms of trend and amplitude of the annual cycle. We also compare the residual time series after removing the trend, the semiannual and the annual signals. To obtain errors for altimetry and Argo, full variance–covariance matrices are computed using correlation functions and their errors are fully propagated. For altimetry, we apply a geographically dependent intermission bias [Ablain et al.(2015)], which leads to differences in trends up to 0.8 mm yr−1. Since Argo float measurements are non-homogeneously spaced, steric sea levels are first objectively interpolated onto a grid before averaging. For the Gravity Recovery And Climate Experiment (GRACE), gravity fields full variance–covariance matrices are used to propagate errors and statistically filter the gravity fields. We use four different filtered gravity field solutions and determine which post-processing strategy is best for budget closure. As a reference, the standard 96 degree Dense Decorrelation Kernel-5 (DDK5)-filtered Center for Space Research (CSR) solution is used to compute the mass component (MC). A comparison is made with two anisotropic Wiener-filtered CSR solutions up to degree and order 60 and 96 and a Wiener-filtered 90 degree ITSG solution. Budgets are computed for 10 polygons in the North Atlantic Ocean, defined in a way that the error on the trend of the MC plus steric sea level remains within 1 mm yr−1. Using the anisotropic Wiener filter on CSR gravity fields expanded up to spherical harmonic degree 96, it is possible to close the sea level budget in 9 of 10 sub-basins in terms of trend. Wiener-filtered Institute of Theoretical geodesy and Satellite Geodesy (ITSG) and the standard DDK5-filtered CSR solutions also close the trend budget if a glacial isostatic adjustment (GIA) correction error of 10–20 % is applied; however, the performance of the DDK5-filtered solution strongly depends on the orientation of the polygon due to residual striping. In 7 of 10 sub-basins, the budget of the annual cycle is closed, using the DDK5-filtered CSR or the Wiener-filtered ITSG solutions. The Wiener-filtered 60 and 96 degree CSR solutions, in combination with Argo, lack amplitude and suffer from what appears to be hydrological leakage in the Amazon and Sahel regions. After reducing the trend, the semiannual and the annual signals, 24–53 % of the residual variance in altimetry-derived sea level time series is explained by the combination of Argo steric sea levels and the Wiener-filtered ITSG MC. Based on this, we believe that the best overall solution for the MC of the sub-basin-scale budgets is the Wiener-filtered ITSG gravity fields. The interannual variability is primarily a steric signal in the North Atlantic Ocean, so for this the choice of filter and gravity field solution is not really significant.
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Turk, Neelam, Sangeeta Kamboj, Sonam Khera, and Neha Rajput. "Performance Analysis of Different Restoration Techniques for Degraded 3D Cervical Spine Mri Image." Biomedical and Pharmacology Journal 14, no. 02 (June 28, 2021): 853–67. http://dx.doi.org/10.13005/bpj/2188.
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In the proposed work, noise models such as Salt & Pepper, Gaussian and Poisson are considered in order to corrupt the image.Image restoration is still challenging task to recover an original image using a degradation and restoration model. In the paper, Gaussian, Average and Wiener linear image restoration techniques are used to recover the original MRI image. Median filter, Min and Max non-linear filters are also used to obtain uncorrupt image in the paper.Mean square error (MSE), Peak Signal to Noise Ratio (PSNR) and Cross Correlation(CC) performance analysis criteria are used to compare different restoration technique so that better performance in a clinical diagnosis can be achieved. In the paper it can be found that wiener filter with 5 x 5 window for Gaussian, speckle and Poisson noise provides best performance in terms of MSE and PSNR. Also,median filter with 5 x 5 window gives better accuracy of results to restore 3D salt & pepper noised image in terms of MSE and CC.
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Lee, Rok-Kyu, and Je-Chang Jeong. "Wiener Filter Based Denoising Algorithm for Demosaicking." Journal of Korea Information and Communications Society 36, no. 5C (May 31, 2011): 286–94. http://dx.doi.org/10.7840/kics.2011.36c.5.286.
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Cheng, Yuan Yuan, Hai Yan Li, Qi Xiao, Yu Feng Zhang, and Xin Ling Shi. "Gaussian Noise Filter Using Variable Step Time Matrix of PCNN." Applied Mechanics and Materials 48-49 (February 2011): 551–54. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.551.
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A novel method was brought forward for the purpose of filtering Gaussian noise effectively by using variable step time matrix of the simplified pulse coupled neural network (PCNN). Firstly, the time matrix of PCNN, related to the grayscale and spatial information of an image, is calculated to identify the noise polluted pixels. Subsequently, a variable step, a long step for strong noise and a short step for weak noise, based on the time matrix is applied to modify the grayscale of noised pixels in a sliding window. And then wiener filter is used to the image to further filter the noise. Experiments show that the proposed filter can remove Gaussian noise effectively than other noise reduction methods such as median filter, mean filter, wiener filter etc, and the filtered image is smooth and the details and edges are sharp. Compared with existing PCNN based Gaussian noise filter, the proposed filter gets higher Peak Signal-to-Noise Ratio (PSNR) and better performance.
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Reguzzoni, M. "From the time-wise to space-wise GOCE observables." Advances in Geosciences 1 (July 18, 2003): 137–42. http://dx.doi.org/10.5194/adgeo-1-137-2003.
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Abstract. The observables of the GOCE three-axis gradiometer are taken in time, along the orbit and, in the timewise proposal, filtered to stay in the measurement bandwidth, i.e. in the frequency interval between 0.005 and 0.1Hz. As a consequence, the resulting “observable" is a convolution of the original data stream with a time-wise weighting kernel. In other words, we cannot assume that the observations are point-wise evaluations of any function and so, in a spacewise approach, any averaging or interpolating operation to obtain gridded spatial data has little sense. The problem is therefore to model correctly the observational functionals, including the correlation along the orbit. This can be done by exploiting theWiener filter theory, using the prior knowledge of a geopotential model and the power spectral density (PSD) of the measurement error. A numerical simulation from the EGM96 model (degrees between 50 and 300) is performed, showing that the along track Wiener filter produces Trr spatialized observables with an error standard deviation of the order of 5 mE. A covariance function of the estimation error is also provided by the Wiener filter theory. The use of these filtered observables in a space-wise approach allows for the reconstruction of the gravity field in terms of spherical harmonics up to degree 200.Key words. Satellite gradiometry, space-wise approach, measurement bandwidth, Wiener filter
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Ventosa, Sergi, Sylvain Le Roy, Iréne Huard, Antonio Pica, Hérald Rabeson, Patrice Ricarte, and Laurent Duval. "Adaptive multiple subtraction with wavelet-based complex unary Wiener filters." GEOPHYSICS 77, no. 6 (November 1, 2012): V183—V192. http://dx.doi.org/10.1190/geo2011-0318.1.
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Adaptive subtraction is a key element in predictive multiple-suppression methods. It minimizes misalignments and amplitude differences between modeled and actual multiples, and thus reduces multiple contamination in the data set after subtraction. Due to the high crosscorrelation between their waveforms, the main challenge resides in attenuating multiples without distorting primaries. As they overlap on a wide frequency range, we split this wide-band problem into a set of more tractable narrow-band filter designs, using a 1D complex wavelet frame. This decomposition enables a single-pass adaptive subtraction via complex, single-sample (unary) Wiener filters, consistently estimated on overlapping windows in a complex wavelet transformed domain. Each unary filter compensates for amplitude differences within its frequency support, and can correct small and large misalignment errors through phase and integer delay corrections. This approach greatly simplifies the matching filter estimation and, despite its simplicity, narrows the gap between 1D and standard adaptive 2D methods on field data.
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Ishizuka, Kazuo, Paul H. C. Eilers, and Toshihiro Kogure. "Optimal Noise Filters in High-Resolution Electron Microscopy." Microscopy Today 15, no. 5 (September 2007): 16–21. http://dx.doi.org/10.1017/s1551929500061186.
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Most of the specimens for high-resolution electron microscopy have amorphous surface layers due to contamination during observation and/or damaged surface layers during specimen preparation. Moreover, many specimens are radiation sensitive, and a part of the specimen easily becomes amorphous during the observation. These amorphous materials make clear observation of crystal structure difficult. A periodic structure may be extracted by simply using a periodic mask in Fourier space. However, this kind of mask often introduces a periodic feature in addition to the crystal structure. To reduce such artifacts a Wiener filter or an average background subtraction filter has been discussed. However, these filters do not work for non-ideal crystals, such as cylindrical crystals and nano-crystals, where a translational periodicity is limited to the order of nano-meter. In this report we improve these filters by introducing new ways to estimate a contribution from the amorphous materials.
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Virk, Amardeep Singh, Mandeep Kaur, and Lovely Passrija. "Performance Evaluation of Image Enhancement Techniques in Spatial and Wavelet Domains." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 1 (August 1, 2012): 162–66. http://dx.doi.org/10.24297/ijct.v3i1c.2771.
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Denoising is one of the important tasks in image processing. Despite the significant research conducted on this topic, the development of efficient denoising methods is still a compelling challenge. In this paper, spatial domain methods and Wavelet Domain Methods of image denoising have been evaluated. The medical ultrasound images suffer from speckle noise which is multiplicative in nature and more difficult to remove than additive noise. In the spatial filter methods Median Filter and Wiener Filter are implemented. These methods are based on the simple formulas that are proposed by different authors. In Wavelet Methods Visu Shrink, Neigh shrink and Bayes Shrink are implemented. The basic idea of wavelet methods is to denoise the image by applying wavelet transform to the noisy image, then thresholding the detailed wavelet coefficient and inverse transforming the set of thresholded coefficient to obtain the denoised image. The comparison of all filters methods is done using various Quality Metrics like Peak Signal-to-Noise Ratio (PSNR), Bit Error Rate (BER), Mean Square Error, etc. The filters methods implemented in MATLAB 7.10.0.499(R2010a).
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Bahar, Arash, Ali Chaibakhsh, and Sajad Haqdadi. "Identification of MR Damper Based on Normalized Bouc-Wen Model Using Neural Network." Applied Mechanics and Materials 229-231 (November 2012): 2140–44. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.2140.
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The Magneto-rheological (MR) dampers are favorite mechanical system in dynamic structures. This paper presents an application of Wiener-type nonlinear models for describing the hysteresis behaviors of MR dampers at different operating conditions. In this structure, a linear part consisting discrete-time Kautz filters is cascading by a nonlinear mapping function (feedforward neural network (FFNN)). The pole parameters of Kautz filters were chosen with respect to the poles of best fitted linear model on real system. By defining the parameters of Kautz filter, the nonlinear behaviors of system were identified using neural network model, as the output of filters were considered as the output on NN. In order to assess the performances of the developed models a comparison between the responses of the models and another recent modeling approach was preformed.
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Hassan, Raaid N. "A comparison between PCA and some enhancement filters for denoising astronomical images." Iraqi Journal of Physics (IJP) 11, no. 22 (February 20, 2019): 82–92. http://dx.doi.org/10.30723/ijp.v11i22.356.
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This paper includes a comparison between denoising techniques by using statistical approach, principal component analysis with local pixel grouping (PCA-LPG), this procedure is iterated second time to further improve the denoising performance, and other enhancement filters were used. Like adaptive Wiener low pass-filter to a grayscale image that has been degraded by constant power additive noise, based on statistics estimated from a local neighborhood of each pixel. Performs Median filter of the input noisy image, each output pixel contains the Median value in the M-by-N neighborhood around the corresponding pixel in the input image, Gaussian low pass-filter and Order-statistic filter also be used.
Experimental results shows LPG-PCA method gives better performance, especially in image fine structure preservation, compared with other general denoising algorithms.
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Kumar, M., S. K. Mishra, and S. S. Sahu. "Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images." Applied Computational Intelligence and Soft Computing 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/6304915.
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Gaussian noise is one of the dominant noises, which degrades the quality of acquired Computed Tomography (CT) image data. It creates difficulties in pathological identification or diagnosis of any disease. Gaussian noise elimination is desirable to improve the clarity of a CT image for clinical, diagnostic, and postprocessing applications. This paper proposes an evolutionary nonlinear adaptive filter approach, using Cat Swarm Functional Link Artificial Neural Network (CS-FLANN) to remove the unwanted noise. The structure of the proposed filter is based on the Functional Link Artificial Neural Network (FLANN) and the Cat Swarm Optimization (CSO) is utilized for the selection of optimum weight of the neural network filter. The applied filter has been compared with the existing linear filters, like the mean filter and the adaptive Wiener filter. The performance indices, such as peak signal to noise ratio (PSNR), have been computed for the quantitative analysis of the proposed filter. The experimental evaluation established the superiority of the proposed filtering technique over existing methods.
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Zhou, Cui, Jing Hong Zhou, and Dong Hao Fan. "The Study of Combination Filtering for Removing Mixed Noise of Remote Sensing Image." Advanced Materials Research 989-994 (July 2014): 3881–84. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3881.
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The traditional combination filter method in the practical process is often two layers of composite filtering de-noising for the image containing the gaussian mixture noise and pulse noise. This paper proposes a three filter combination algorithm based on the two layers filters, To the Gaussian and pulse mixed noise of the remote sensing image, we use wavelet threshold de-nosing and adaptive median filter de-nosing, and then using the third layer adaptive Wiener filtering de-noising to remove the residual noise. Through theoretical analysis and practical application, the de-nosing results of this method is obvious in processing mixed noise of remote sensing image, this is a practical method of combination filtering de-noising, it can be widely used in the field of image processing.
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Saselah, Gybert, Winsy Weku, and Luther Latumakulita. "Perbaikan Citra Digital dengan Menggunakan Filtering Technique dan Similarity Measurement." d'CARTESIAN 2, no. 2 (October 1, 2013): 1. http://dx.doi.org/10.35799/dc.2.2.2013.3203.
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Abstract Often the digital image can be contaminated with noise, which usually occurs in the process of retrieval or storage of digital images and delivery process either via satellite or cable . By using the technique of filtering noise reduction process will be performed on a digital image that has previously been given Gaussian noise and followed by a Similarity Measurement to identify similarities between image filtered and original image. This study was conducted to determine the appropriate filtering techniques to reduce the Gaussian noise. Image processing in this study composed by the input image and read the image matrix, converting images, adding noise, denoising digital images by applying filters performed using Matlab R2012a software ( version 7.14.0.739) . Application of Gaussian filter with a value of = 1.0 produce a digital image that is closest to the original image than the application of a Gaussian filter with another value, for . As for the application of the Wiener filter is seen that the greater the value, the resulting digital image will be closer to the original image. For further research can be done on other types of noise or to a combination of two or more noise. Keywords : Digital Image , Noise , Filter , Similarity Measurement. Abstrak Seringkali citra digital dapat terkontaminasi derau (noise), yang biasanya terjadi pada proses pengambilan ataupun penyimpanan citra digital serta proses pengiriman citra digital baik melalui satelit maupun melalui kabel juga. Dengan menggunakan teknik filtering akan dilakukan proses pengurangan noise pada suatu citra digital yang sebelumnya telah diberi Gaussian noise dan dilanjutkan dengan Similarity Measurement untuk mengidentifikasi kesamaan citra digital hasil filtering dengan citra original. Penelitian ini dilakukan untuk menentukan teknik filtering yang tepat untuk mengurangi Gaussian noise. Proses pengolahan citra dalam penelitian ini terdiri dengan proses input gambar dan membaca matriks citra, konversi citra, menambahkan noise, denoising citra digital dengan menerapkan filter yang dilakukan dengan menggunakan software Matlab R2012a (versi 7.14.0.739). Penerapan Gaussian filter dengan nilai = 1,0 menghasilkan citra digital yang paling mendekati citra original dibandingkan dengan penerapan Gaussian filter dengan nilai lain, dimana . Sedangkan untuk penerapan Wiener filter terlihat bahwa semakin besar nilai , maka citra digital yang dihasilkan akan semakin mendekati citra original. Untuk penelitian selanjutnya dapat dilakukan pada jenis noise lain ataupun untuk gabungan dua noise atau lebih. Kata kunci: Citra digital, Noise, Filter, Similarity Measurement
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Pawlowski, R. S., and R. O. Hansen. "Gravity anomaly separation by Wiener filtering." GEOPHYSICS 55, no. 5 (May 1990): 539–48. http://dx.doi.org/10.1190/1.1442865.
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We introduce a gravity anomaly separation method based on frequency‐domain Wiener filtering. Gravity anomaly separation can be effected by such wavelength filtering when the gravity response from the geologic feature of interest (the signal) dominates one region (or spectral band) of the observed gravity field’s power spectrum. The Wiener filter is preferable to a conventional band‐pass filter because geologic information from the study area can be incorporated to a greater extent in specifying the filter’s transfer function. Our method differs from previous Wiener filtering schemes in that it provides, through direct modeling of known geology (e.g., outcrop and borehole data), a more objective estimate of the signal power spectrum required for defining the transfer function of the filter. We illustrate the technique first with synthetic data, and then with a field example from the southern Paradox basin. The Paradox basin example reveals the limitation inherent to all wavelength filtering which results from spectral overlap between the gravity signal and the spectral contributions of other geologic sources. In the study area, significant spectral overlap occurs between the gravity effects of sources in the Precambrian basement and the gravity signal arising from the density contrast across the Mississippian‐Pennsylvanian interface.
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Khoury, Jehad, Peter D. Gianino, and Charles L. Woods. "Wiener-like correlation filters." Applied Optics 39, no. 2 (January 10, 2000): 231. http://dx.doi.org/10.1364/ao.39.000231.
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Yan, Man, and Yong Quan Liang. "Combined Filter Method Based on Terrain Factors." Applied Mechanics and Materials 596 (July 2014): 342–45. http://dx.doi.org/10.4028/www.scientific.net/amm.596.342.
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Interferogram noise filter is one of the key technologies which use synthetic aperture radar interferometry phase to extract digital elevation model. However, the interference fringes’ details are often reduced when complex interferogram is filtered, and this leads to inaccurate phase unwrapping result, for this combined filter method based on terrain factors is proposed. Filter method can be selected through weight factor associated with the terrain slope in the filter process, and Wiener filter method is implemented in flat terrain area, frequency estimated filter method is achieved in steep terrain area. Then the new filter method is applied to deal with data where the region of Quebec, Canada. This proves that the new filter algorithm proposed in this paper can effectively suppress noise and better maintain stripe details.
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Ledesma, Dorian Caraballo, and Milton José Porsani. "DECONVOLUTION OF SEISMIC DATA USING PHASE CHANGE OPERATORS." Revista Brasileira de Geofísica 31, no. 1 (March 1, 2013): 59. http://dx.doi.org/10.22564/rbgf.v31i1.246.
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This paper presents a new approach for wavelet deconvolution. We investigate the use of all-pass operator to change the phase of the minimum-phase inverse filters. The all-pass operators are formed by using the roots of the polynomial associated to the Wiener-Levinson filters. By selecting subsets of roots itis possible to generate an ensemble of mixed-phase inverse filters. The optimization process to obtain an optimum inverse filter is performed by using a genetic algorithm. We have used the varimax norm as the objective function to measure the simplicity of the seismic trace after deconvolution. The method was tested using synthetic and real seismic data. RESUMO: Apresentamos uma nova abordagem para deconvolução do pulso sísmico. Investigamos o uso de operadores de mudança de fase para obtenção de filtros de fase mista a partir de filtros de fase mínima. Os operadores de mudança de fase são construídos a partir das raízes associadas aos filtros de Wiener-Levinson. Escolhendo subconjuntos daquelas raízes é possível gerar um conjunto de filtros inversos de fase mista. A escolha do filtro ótimo é realizada através do método de otimização conhecido com algoritmo genético. Para tanto utilizamos a norma varimax para medida da simplicidade do traço deconvolvido. O método foi testado utilizando dados sísmicos sintéticos e reais.Palavras-chave: deconvolução de fase mista; processamento sísmico; estimativa do pulso sísmico
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Farhi, Lubna, Agha Yasir, Farhan Ur Rehman, Baqar A. Zardari, and Ramsha Shakeel. "Optimized Hybrid Model for Gaussian Noise Reduction images." Quaid-e-Awam University Research Journal of Engineering Science & Technology 19, no. 1 (June 30, 2021): 9–17. http://dx.doi.org/10.52584/qrj.1901.02.
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In this paper, image noise is removed by using a hybrid model of wiener and fuzzy filters. It is a challenging task to remove Gaussian noise (GN) from an image and to protect the image’s edges. The Fuzzy-Wiener filter (FWF) hybrid model is used for optimizing the image smoothness and efficiency at a high level of GN. The efficiency is measured by using Structural Similarity (SSIM), Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). The proposed algorithm substitutes a mean value of the matrix for a non-overlapping block and replaces the total pixel number with each direction. In the proposed model, overall results proved that the optimized hybrid model FWF has an enormous computational speed and impulsive noise reduction, which enables efficient filtering as compared to the existing techniques.
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Bazine, Razika, Huayi Wu, and Kamel Boukhechba. "Spatial Filtering in DCT Domain-Based Frameworks for Hyperspectral Imagery Classification." Remote Sensing 11, no. 12 (June 13, 2019): 1405. http://dx.doi.org/10.3390/rs11121405.
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In this article, we propose two effective frameworks for hyperspectral imagery classification based on spatial filtering in Discrete Cosine Transform (DCT) domain. In the proposed approaches, spectral DCT is performed on the hyperspectral image to obtain a spectral profile representation, where the most significant information in the transform domain is concentrated in a few low-frequency components. The high-frequency components that generally represent noisy data are further processed using a spatial filter to extract the remaining useful information. For the spatial filtering step, both two-dimensional DCT (2D-DCT) and two-dimensional adaptive Wiener filter (2D-AWF) are explored. After performing the spatial filter, an inverse spectral DCT is applied on all transformed bands including the filtered bands to obtain the final preprocessed hyperspectral data, which is subsequently fed into a linear Support Vector Machine (SVM) classifier. Experimental results using three hyperspectral datasets show that the proposed framework Cascade Spectral DCT Spatial Wiener Filter (CDCT-WF_SVM) outperforms several state-of-the-art methods in terms of classification accuracy, the sensitivity regarding different sizes of the training samples, and computational time.
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McElroy, Tucker S., and Agustin Maravall. "Optimal Signal Extraction with Correlated Components." Journal of Time Series Econometrics 6, no. 2 (July 1, 2014): 237–73. http://dx.doi.org/10.1515/jtse-2013-0016.
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AbstractWhile it is typical in the econometric signal extraction literature to assume that the unobserved signal and noise components are uncorrelated, there is nevertheless an interest among econometricians in the hypothesis of hysteresis, i.e. that major movements in the economy are fundamentally linked. While specific models involving correlated signal and noise innovation sequences have been developed and applied using state space methods, there is no systematic treatment of optimal signal extraction with correlated components. This paper provides the mean square error optimal formulas for both finite samples and bi-infinite samples and furthermore relates these filters to the more well-known Wiener–Kolmogorov (WK) and Beveridge–Nelson (BN) signal extraction formulas in the case of ARIMA component models. Then we obtain the result that the optimal filter for correlated components can be viewed as a weighted linear combination of the WK and BN filters. The gain and phase functions of the resulting filters are plotted for some standard cases. Some discussion of estimation of hysteretic models is presented, along with empirical results on an economic time series. Comparisons are made between signal extractions from traditional WK filters and those arising from the hysteretic models.
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Xi, Zaifang, Xiao-feng Wu, Shuyue Wu, Zhijun Tang, and Shigang Hu. "Low-Complexity Robust Capon Beamforming Based on Reduced-Rank Technique." Journal of Electrical and Computer Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/583075.
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Existing robust Capon beamformers achieve robustness against steering vector errors at a high cost in terms of computational complexity. Computationally efficient robust Capon beamforming approach based on the reduced-rank technique is proposed in this paper. The proposed method projects the received data snapshots onto a lower dimensional subspace consisting of the matched filters of the multistage Wiener filter (MSWF). The subsequent adaptive beamforming will then be performed within this subspace. The combination of the benefit of the robust adaptive beamforming and the reduced-rank technique improves the performance on combating steering vector errors and lowering the computational complexity.
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Zalevsky, Zeev, and David Mendlovic. "Fractional Wiener filter." Applied Optics 35, no. 20 (July 10, 1996): 3930. http://dx.doi.org/10.1364/ao.35.003930.
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Xi, Bin, and Yuehong Liu. "Iterative Wiener filter." Electronics Letters 49, no. 5 (February 2013): 343–44. http://dx.doi.org/10.1049/el.2013.0009.
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Kiwata, Hirohito. "Optimized Wiener filter." Journal of Physics A: Mathematical and Theoretical 45, no. 35 (August 13, 2012): 355101. http://dx.doi.org/10.1088/1751-8113/45/35/355101.
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Dogariu, Laura-Maria, Constantin Paleologu, Jacob Benesty, Cristian-Lucian Stanciu, Claudia-Cristina Oprea, and Silviu Ciochină. "A Kalman Filter for Multilinear Forms and Its Connection with Tensorial Adaptive Filters." Sensors 21, no. 10 (May 20, 2021): 3555. http://dx.doi.org/10.3390/s21103555.
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The Kalman filter represents a very popular signal processing tool, with a wide range of applications within many fields. Following a Bayesian framework, the Kalman filter recursively provides an optimal estimate of a set of unknown variables based on a set of noisy observations. Therefore, it fits system identification problems very well. Nevertheless, such scenarios become more challenging (in terms of the convergence and accuracy of the solution) when the parameter space becomes larger. In this context, the identification of linearly separable systems can be efficiently addressed by exploiting tensor-based decomposition techniques. Such multilinear forms can be modeled as rank-1 tensors, while the final solution is obtained by solving and combining low-dimension system identification problems related to the individual components of the tensor. Recently, the identification of multilinear forms was addressed based on the Wiener filter and most well-known adaptive algorithms. In this work, we propose a tensorial Kalman filter tailored to the identification of multilinear forms. Furthermore, we also show the connection between the proposed algorithm and other tensor-based adaptive filters. Simulation results support the theoretical findings and show the appealing performance features of the proposed Kalman filter for multilinear forms.
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Devi, R., and P. Sujatha. "Enhancement of fingerprint image using wiener filter." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 206. http://dx.doi.org/10.14419/ijet.v7i1.1.9456.
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A fingerprint is one of the most vital Biometric traits used for Personal Identification. To identify and match the fingerprint accurately, it has to be enhanced efficiently. In this paper, an efficient fingerprint enhancement technique is adopted and compared with the existing methods. The proposed methodology consists of three Phases. In the first phase, the fingerprint is subjected to the de-noising process. After adding noise such as salt and pepper, Gaussian and speckle noise, the image is blurred. In the second phase, the fingerprint is filtered with Wiener filter and then de-blurred. In the third, the filtered image is further enhanced for more clarity. The paper emphasizes, the fingerprint preprocessing followed with the enhancement produces better quality image. The performance of the proposed methodology is compared and evaluated using two performances measures namely Peak-Signal-Noise –Ratio and Mean Squared Error using Matlab R2013a.
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Kavinkumar, K., and T. Meeradevi. "Classification of Tumor of MRI Brain Image Using Hybrid Feature Extraction Method and Support Vector Machine Classifier." Journal of Medical Imaging and Health Informatics 11, no. 10 (October 1, 2021): 2558–65. http://dx.doi.org/10.1166/jmihi.2021.3842.
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Brain tumors Analysis is problematic somewhat due to varied size, shape, location of tumor and the appearance and presence of brain tumor. Clinicians and radiologist have difficulty in identifying the tumor type. An efficient hybrid feature extraction method to classify the type of tumor accurately as meningioma, gliomas and pituitary tumor using SVM (support vector machine) classifier is proposed. The modified Non-Local Means (NLM) filter may be effectively used to get the pure image. The NLM filter is compared with common filters like median and wiener. From the denoised image the classification is done by training SVM using the texture features from the hybrid and efficient feature extraction technique.The accuracy of the classification is calculated and the SVM classifier training individual type of texture features and also with combined texture features and the performance is analyzed.
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Jiang, Shiaw-Shiang, and Alexander A. Sawchuk. "Noise updating repeated Wiener filter and other adaptive noise smoothing filters using local image statistics." Applied Optics 25, no. 14 (July 15, 1986): 2326. http://dx.doi.org/10.1364/ao.25.002326.
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Cervantes Ávila, Aquilino. "Filtros Wiener." Polibits 19 (January 20, 1998): 8–12. http://dx.doi.org/10.17562/pb-19-2.
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McCarley, Lon A. "An autoregressive filter model for constant Q attenuation." GEOPHYSICS 50, no. 5 (May 1985): 749–58. http://dx.doi.org/10.1190/1.1441949.
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The Earth’s filter response to seismic wave propagation can be approximated by constant Q attenuation and is dispersive or minimum phase. A finite‐length autoregressive (AR) filter model is a good representation for constant Q attenuation with minimum phase. Coefficients of the AR filter are the Wiener‐Levinson inverse filter coefficients for the sampled constant Q auto‐correlation. Conventional spike deconvolution approximates fairly well the inverse filter on this ground if the minimum‐phase attenuation law holds true. Wavelet response to attenuation was analyzed using the AR filter model. The amplitude of the filtered impulse response decreases at nearly 1/t, where t is traveltime, and is sensitive to the loss of low frequencies. The wavelets’ peak amplitude is fractionally time delayed with .92/Q. Velocity dispersion of seismic waves should not contribute to the mis‐tie observed between the conventional check shot and the borehole sonic log when the first arrivals are picked the same way.
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BAGGIO, L., M. CERDONIO, A. ORTOLAN, G. VEDOVATO, V. MARTINUCCI, G. A. PRODI, S. VITALE, L. TAFFARELLO, and J. P. ZENDRI. "ON-LINE CONSISTENCY TESTS FOR BAR DETECTORS." International Journal of Modern Physics D 09, no. 03 (June 2000): 251–55. http://dx.doi.org/10.1142/s0218271800000232.
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In order to detect gravitational wave signals with resonant bar detectors using Wiener–Kolmogorov (WK) filters, both a model for the power spectrum density (PSD) of the noise and a signal template should be provided. As the analysis is not meant to handle non-gaussian data, we have to discriminate (and constrain to) time periods where the noise follows a quasi-stationary gaussian model. Within these periods, candidate events are selected in the WK filter output, and their fundamental parameters (time of arrival and amplitude) are computed. A necessary and sufficient condition for the reliability of such estimates is the consistency of the signal shape with the template. This is done performing a goodness-of-the-fit test.