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Artykuły w czasopismach na temat „Wavelet Transform and Wavelet Thresholding”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Umam, Ahmad Khairul, Pukky Tetralian Bantining Ngastiti, Aris Alfan, Zaqiyatus Shahadah, and Amanda Fatma Muamalah. "TRANSFORMASI WAVELET DISKRIT UNTUK DENOISING CITRA." MATHunesa: Jurnal Ilmiah Matematika 12, no. 2 (2024): 374–80. https://doi.org/10.26740/mathunesa.v12n2.p374-380.

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Nowadays, topic of wavelet has many applications including image denoising. Wavelet Transform is divided into discrete wavelet transform and continuous wavelet transform. Besides for image denoising, it can also useful for image compression and others. In this research is discussed about steps image denoising using wavelet. Wavelets that used are Haar, Daubechies, biorthogonal, symlets and coiflets wavelets for hard thresholding and soft thresholding. Program is made according to steps/algorithm that were created. Then, we compare original image and result of image denoising. In this research,

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Raath, Kim C., Katherine B. Ensor, Alena Crivello, and David W. Scott. "Denoising Non-Stationary Signals via Dynamic Multivariate Complex Wavelet Thresholding." Entropy 25, no. 11 (2023): 1546. http://dx.doi.org/10.3390/e25111546.

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Over the past few years, we have seen an increased need to analyze the dynamically changing behaviors of economic and financial time series. These needs have led to significant demand for methods that denoise non-stationary time series across time and for specific investment horizons (scales) and localized windows (blocks) of time. Wavelets have long been known to decompose non-stationary time series into their different components or scale pieces. Recent methods satisfying this demand first decompose the non-stationary time series using wavelet techniques and then apply a thresholding method

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ČASTOVÁ, NINA, DAVID HORÁK, and ZDENĚK KALÁB. "DESCRIPTION OF SEISMIC EVENTS USING WAVELET TRANSFORM." International Journal of Wavelets, Multiresolution and Information Processing 04, no. 03 (2006): 405–14. http://dx.doi.org/10.1142/s0219691306001336.

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This paper deals with engineering application of wavelet transform for processing of real seismological signals. Methodology for processing of these slight signals using wavelet transform is presented in this paper. Briefly, three basic aims are connected with this procedure:. 1. Selection of optimal wavelet and optimal wavelet basis B opt for selected data set based on minimal entropy: B opt = arg min B E(X,B). The best results were reached by symmetric complex wavelets with scaling coefficients SCD-6. 2. Wavelet packet decomposition and filtration of data using universal criterion of thresho

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Dahraoui, Nadia, M'hamed Boulakroune, S. Khelfaoui, S. Kherroubi, and Yamina Benkrima. "Effectiveness of Wavelet Denoising on Secondary Ion Mass Spectrometry Signals." East European Journal of Physics, no. 3 (September 4, 2023): 495–500. http://dx.doi.org/10.26565/2312-4334-2023-3-56.

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Wavelet theory has already achieved huge success. For Secondary Ions Mass Spectrometry (SIMS) signals, denoising the secondary signal, which is altered by the measurement, is considered that an essential step prior to applying such a signal processing technique that aims enhance the SIMS signals.The most efficient and widely used wavelet denoising method is based on wavelet coefficient thresholding. This process involves three important steps; wavelet decomposition: the input signals are decomposed into wavelet coefficients, thresholding: the wavelet coefficients are modified according to a th

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Saidov, B. B., and V. F. Telezhkin. "Optimum ECG Signal Filtering Based on Wavelet Transformation." Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics 21, no. 4 (2021): 167–72. http://dx.doi.org/10.14529/ctcr210415.

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The development of digital signal processing and microprocessor technology creates conditions for improving methods for diagnosing the functional state of organs. Wavelet analysis is a modern and promising method of information processing. In order to determine the effective optimal filtering of the electrocardiography signal based on the wavelet transform, wavelet filtering was performed using wavelets of different families, the efficiency of using different levels of decomposition, me¬thods for calculating the threshold and types of the threshold function was investigated. Aim. Determination

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Saood, Wesam Hujab, and Khamees Khalaf Hasan. "The Effectiveness of two Dimensional Haar Wavelet Image De-noising performance using Soft or Hard Thresholding Approach." Tikrit Journal of Pure Science 29, no. 1 (2024): 196–205. http://dx.doi.org/10.25130/tjps.v29i1.1507.

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Image de-noising and restoration represent basic problems in image processing with many different applications including engineering, reconstruction of missing data during their transmission and enhancement ..etc. this work is aimed at developing effective algorithm for denoising image using new strategy algorithm of wavelet techniques ,by applying two dimensions wavelet transform using Haar wavelet. Wavelets are hierarchically decomposing mathematical tools. A noisy picture is sent to the Haar wavelet transform to create four decomposed bands(for each level), and the noise is then removed by

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Ramadhan, Rahmat, and Agfianto Eko Putra. "Perbandingan Mother Wavelet dalam Proses Denoising pada Suara." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 8, no. 1 (2014): 69. http://dx.doi.org/10.22146/ijccs.3497.

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AbstrakTransformasi Wavelet telah digunakan dalam proses denoising pada suara dengan tujuan untuk meningkatkan kualitas dari rekaman suara yang tercampur dengan derau. Jenis-jenis derau yang terlibat antara lain White Gaussian Noise (WGN), White Uniform Noise (WUN) dan Colored Noise. Dalam penelitian ini dilakukan perbandingan terhadap beberapa mother wavelet, diantaranya Daubechies, Coiflet dan Symlet, dalam proses denoising pada sinyal suara yang diberikan WGN, WUN dan Colored Noise. Metode thresholding yang digunakan dalam proses denoising adalah Soft Thresholding dan nilai threshold berupa

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Shruti, Badgainya, Pankaj Sahu Prof., and Vipul Awasthi Prof. "Image Denoising for AWGN Corrupted Image Using OWT and Thresholding." International Journal of Trend in Scientific Research and Development 2, no. 6 (2018): 220–26. https://doi.org/10.31142/ijtsrd18338.

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In this work, review of various well known algorithms for image denoising is carried out and their performances with their methodologies are comparatively assessed. A new algorithm based on the orthonormal wavelet transform OWT is developed. In this work images corrupted by AWGN are denoised. Simulation results shows that proposed method using Orthonormal wavelets for different values of noise Standard Deviation s in dB outperforms other available methods. Also Coiflet Wavelet performs better than Symlet, Haar and Daubechies wavelets. The proposed Orthonormal wavelet transform OWT method has m

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Nigam, Vaibhav, Smriti Bhatnagar, and Sajal Luthra. "Image Denoising Using Wavelet Transform and Wavelet Transform with Enhanced Diversity." Advanced Materials Research 403-408 (November 2011): 866–70. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.866.

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This paper is a comparative study of image denoising using previously known wavelet transform and new type of wavelet transform, namely, Diversity enhanced discrete wavelet transform. The Discrete Wavelet Transform (DWT) has two parameters: the mother wavelet and the number of iterations. For every noisy image, there is a best pair of parameters for which we get maximum output Peak Signal to Noise Ratio, PSNR. As the denoising algorithms are sensitive to the parameters of the wavelet transform used, in this paper comparison of DEDWT to DWT has been presented. The diversity is enhanced by compu

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Geetika Agotra and Prof. Manish Kumar Singhal. "A Review of Image Denoising Using Fuzzy and Wiener Filters in the Wavelet Domain." International Journal of Scientific Research in Science and Technology 11, no. 5 (2024): 143–49. http://dx.doi.org/10.32628/ijsrst2411430.

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This paper focuses on image denoising using fuzzy wavelet domain transforms, reviewing recent advancements in this area. Wavelet transforms have become a powerful tool in image denoising, with one of the most widely used techniques involving thresholding wavelet coefficients. The paper proposes a hybrid denoising method that combines the wavelet transform, median filtering, and nonlinear diffusion. Additionally, a novel fuzzy filter is introduced to reduce additive noise in digital color images. Two distinct image denoising techniques are discussed: the first employs an Asymmetrical Triangular

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Prakash, Om, and Ashish Khare. "Medical Image Denoising Based on Soft Thresholding Using Biorthogonal Multiscale Wavelet Transform." International Journal of Image and Graphics 14, no. 01n02 (2014): 1450002. http://dx.doi.org/10.1142/s0219467814500028.

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Recorded medical images often represent a degraded version of the original scene due to imperfections in electronic or photographic medium used. The degradations may have many causes, but two dominant degradations are noise and blur. Restoration of blurred and noisy medical images is of fundamental importance in several medical imaging applications. Most of the medical image denoising techniques need removal of blur before the denoising. Denoising of medical images in presence of blur is a hard problem. Most of the wavelet transform-based denoising techniques use the orthonormal wavelets and s

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Sultan, Nora Hussam. "HYBRID IMAGE DENOISING USING WIENER FILTER WITH DISCRETE WAVELET TRANSFORM AND FRAMELET TRANSFORM." Kufa Journal of Engineering 7, no. 2 (2016): 122–33. http://dx.doi.org/10.30572/2018/kje/721211.

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Removal of noise from an image is an essential part of image processing systems. In this paper a hybrid denoising algorithm which combines spatial domain Wiener filter and thresholding function in the wavelet and framelet domain is done. In this work three algorithms are proposed. The first hybrid denoising algorithm using Wiener filter with 2-level discrete wavelet transform (DWT), the second algorithm its using Wiener filter with 2-level framelet transform (FLT) and the third hybrid denoising algorithm its combines wiener filter with 1-level wavelet transform then apply framelet transform on

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Dehda, Bachir, and Mohammed Salah Mesai Aoun. "An efficient method for image denoising based on a new nonlinear wavelet thresholding function." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e11193. https://doi.org/10.54021/seesv5n2-583.

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Image noise is random variation of brightness or color information in images, and is usually an aspect of electronic noise. It can be produced by the image sensor and circuitry of a scanner or digital camera. In fact, there are different kinds of noise functions such as Gaussian noise, salt and pepper noise and speckle noise. Hence, image denoising is the process of removing noise from an image using one of the denoising methods such as spatial or transform techniques. The main aim of an image denoising technique is to achieve both noise reduction and feature preservation. In this context, the

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Liu, Shou Shan, Chuan Jiang Wang, Li Jun Bi, and Chang Zhi Lv. "Application of Adaptive Wavelet Thresholding to Ultrasonic Signal Compression of Aluminum Alloy Forgings." Advanced Materials Research 658 (January 2013): 89–92. http://dx.doi.org/10.4028/www.scientific.net/amr.658.89.

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In this paper, for the purpose of ultrasonic signal compression and the coherent noise depressing in nondestructive test of aluminum alloy forging, the mathematical model of defect echoes is discussed and confirmed. And then the wavelet kernel is also confirmed according the waveform of the defect echoes. As the algorithms of standard hard thresholding and soft thresholding of wavelet transform can not bring out effective compression and depression to the coherent noise, an adaptive wavelet thresholding algorithm is introduced. Experimental results indicate that the adaptive wavelet thresholdi

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Laavanya Mohan, Vijayaragahvan Veeramani,. "A COMPARATIVE STUDY OF VARIOUS WAVELET APPROACHES USED IN IMAGE DENOISING." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (2021): 1061–78. http://dx.doi.org/10.17762/itii.v9i1.238.

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The world is constantly changing, and vision helps the humans to understand the environmental changes over time. The changes can be seen by, capturing the images. Hence digital image plays a vital role in day to day life. During the process of acquisition of digital image, the qualities of digital pictures are degraded due to additive noise known as adaptive white Gaussian noise. Therefore, the major challenge of image denoising algorithm is to improve the visual appearance while preserving the other details of the image. For the last two decades, wavelet has become an elegant tool in image de

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ZHANG, LEI, XIAOLIN WU, and PAUL BAO. "NOISY SIGNAL COMPRESSION BY WAVELET TRANSFORM WITH OPTIMAL DOWNSAMPLING." International Journal of Wavelets, Multiresolution and Information Processing 01, no. 04 (2003): 407–23. http://dx.doi.org/10.1142/s0219691303000268.

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This paper presents a wavelet transform (WT) based on simultaneous de-noising and compression scheme for noisy signal. Due to the downsampling in decomposition process, the orthogonal wavelet transform (OWT) is translation variant, which significantly hinders its performance in coding and denoising. In this paper the wavelet bintree decomposition (WBD), which is equivalent to a translation invariant WT, is first formed and an optimal downsampling route is then traversed among all the routes of the bintree. The WT with the optimal route would most effectively decorrelate and compactly represent

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Wei, Shan, Chun Juan Ou Yang, and Si Min Wei. "A Novel Denoising Algorithm Based on Fuzzy Clustering and Wavelet Transform." Key Engineering Materials 428-429 (January 2010): 569–72. http://dx.doi.org/10.4028/www.scientific.net/kem.428-429.569.

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According to analyzing the different wavelet coefficients' transmission property of signals and noises under different scales of the wavelet transform, LEFC denoising algorithm based on fuzzy clustering and wavelet transform is proposed. Our experimental evaluations show that the algorithm is effective and robust to restore the images compared with the other wavelet soft-thresholding algorithms. When the ratio exceeds 40 %, LEFC gives superior performance.

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Long, Hoang Viet, Haifa Bin Jebreen, and Stefania Tomasiello. "Multi-Wavelets Galerkin Method for Solving the System of Volterra Integral Equations." Mathematics 8, no. 8 (2020): 1369. http://dx.doi.org/10.3390/math8081369.

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In this work, an efficient algorithm is proposed for solving the system of Volterra integral equations based on wavelet Galerkin method. This problem is reduced to a set of algebraic equations using the operational matrix of integration and wavelet transform matrix. For linear type, the computational effort decreases by thresholding. The convergence analysis of the proposed scheme has been investigated and it is shown that its convergence is of order O(2−Jr), where J is the refinement level and r is the multiplicity of multi-wavelets. Several numerical tests are provided to illustrate the abil

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Ali, Mohammed Nabih. "A wavelet-based method for MRI liver image denoising." Biomedical Engineering / Biomedizinische Technik 64, no. 6 (2019): 699–709. http://dx.doi.org/10.1515/bmt-2018-0033.

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Abstract Image denoising stays be a standout amongst the primary issues in the field of image processing. Several image denoising algorithms utilizing wavelet transforms have been presented. This paper deals with the use of wavelet transform for magnetic resonance imaging (MRI) liver image denoising using selected wavelet families and thresholding methods with appropriate decomposition levels. Denoised MRI liver images are compared with the original images to conclude the most suitable parameters (wavelet family, level of decomposition and thresholding type) for the denoising process. The perf

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Khoo, David Wee Yang, Zhi Qun Ng, and Leong Keey Seah. "A Wavelet-based Filtering Algorithm for Enhancing Signal Processing in Coriolis Flow Meters." EPJ Web of Conferences 323 (2025): 08002. https://doi.org/10.1051/epjconf/202532308002.

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Applying signal processing method effectively for a Coriolis flow meter (CFM) requires robust filtering strategies. This is because in actual bunkering processes, various noise components can be generated resulting in unreliable mass flow rate measurements. This study introduces a wavelet transform-based filtering algorithm to denoise and extract relevant features from non-stationary signals. It can be observed that the db6 and sym6 wavelets, with SURE and FDR thresholding, achieve the highest SNR values and lowest RMSE. The sym6 wavelet & SURE threshold exhibited good noise reduction effe

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Zhang, Chao, and Mirko van der Baan. "Strong random noise attenuation by shearlet transform and time-frequency peak filtering." GEOPHYSICS 84, no. 6 (2019): V319—V331. http://dx.doi.org/10.1190/geo2018-0500.1.

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Directional wavelet transforms combined with coefficient thresholding are very competitive in denoising seismic signals. However, these techniques struggle when the coefficients of signal and noise have comparable magnitudes. To better address this problem, we have developed an improvement to this method by applying time-frequency peak filtering (TFPF) to the directional wavelet coefficients. TFPF consists of computing the instantaneous frequency of a frequency-modulated analytic signal. The use of a longer or shorter smoothing window helps to emphasize either signal or remove random noise. In

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Manandhar, Reena, and Sanjeeb Prashad Pandey. "Echocardiography image denoising using fractal wavelet transform." Journal of Science and Engineering 5 (August 31, 2018): 23–33. http://dx.doi.org/10.3126/jsce.v5i0.22369.

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One of the most important areas in image processing is medical image processing where the quality of the images has become an important issue. Most of the medical images are corrupted with the visual noise, and one of the such images is echocardiography image where this effect is more. So, this research aims to denoise the echocardiography image with fractal wavelet transform and to compare its performance with other wavelet based algorithm like hard thresholding, soft thresholding and wiener filter. Initially, the image is corrupted by the Gaussian noise with varying noise variances and is de

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Koteswara Rao, M., G. Chandra Reddy, and K. V. Rama Rao. "Denoising of Images using Wavelet Transform." International Transactions on Electrical Engineering and Computer Science 4, no. 1 (2025): 32–37. https://doi.org/10.62760/iteecs.4.1.2025.125.

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Denoising images corrupted by noise is a critical task in image processing, where wavelet shrinkage methods have proven to be highly effective. Traditional approaches, such as the SCAD and Soft thresholding functions, are commonly used for noise suppression. This paper proposes a novel shrinkage function designed to improve the accuracy of image denoising. The proposed function is evaluated under various methods, including the Top method, Universal method, and Translation Invariant method, to handle images contaminated by additive white Gaussian noise. Performance is benchmarked against SCAD,

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Gong, Jing. "Wavelet detection method for transient electrical energy disturbance with noise." Journal of Physics: Conference Series 2903, no. 1 (2024): 012011. https://doi.org/10.1088/1742-6596/2903/1/012011.

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Abstract Electric energy signals are often contaminated by noise during the collection process, which affects the accurate positioning of disturbances. The discontinuity of traditional hard threshold functions can lead to oscillations, while excessive smoothness of soft threshold functions can lead to signal distortion. The optimized threshold function and scale adaptive processing on the selection of the threshold are proposed. The wavelet thresholding method is used to process voltage sag, rise, and interrupt signals containing noise. The denoised signal is decomposed into five scale wavelet

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Smrčok, Ľubomír, Marián Ďurík, and Vladimír Jorík. "Wavelet denoising of powder diffraction patterns." Powder Diffraction 14, no. 4 (1999): 300–304. http://dx.doi.org/10.1017/s0885715600010733.

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Four powder diffraction patterns taken under different experimental conditions were denoised by a new method, i.e., thresholding of wavelet coefficients. The patterns were transformed by discrete wavelet transform applying Coiflet4 wavelet function. WLS refinements of peaks’ positions, FWHM, and intensity showed that wavelet denoising, in contrast to previously used polynomial smoothing, did not shift the maxima and preserved peak and integrated intensities. This method may therefore represent an useful alternative to polynomial filters or filters based on Fourier transform.

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Zahmati, Saba, Mohammad Mahdi Khalilzadeh, and Mohsen Foroughipour. "MRI EDGE DETECTION AND NOISE REDUCTION WITH HYBRID SYSTEM BASED ON WAVELET AND CURVELET." Biomedical Engineering: Applications, Basis and Communications 27, no. 03 (2015): 1550024. http://dx.doi.org/10.4015/s1016237215500246.

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In recent years, multi-scale transform application in image processing especially for magnetic resonance (MR) images has been raised. Wavelet transform is introduced as a useful tool in image processing and it is capable of effectively removing noise from magnetic resonance images. The main problem with wavelet transform is that it is not able to distinguish one dimensional (1D) or higher dimentional discontinuities in images. In other words, since the wavelet transform is two dimensional (2D), it is considered as a separable transform, it is solely able to identify pointwise discontinuity in

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Alfaouri, Mikhled, and Khaled Daqrouq. "ECG Signal Denoising By Wavelet Transform Thresholding." American Journal of Applied Sciences 5, no. 3 (2008): 276–81. http://dx.doi.org/10.3844/ajassp.2008.276.281.

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Zhang, Lin, Xiaomou Zhou, Zhongbin Wang, Chao Tan, and Xinhua Liu. "A Nonmodel Dual-Tree Wavelet Thresholding for Image Denoising Through Noise Variance Optimization Based on Improved Chaotic Drosophila Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 08 (2017): 1754015. http://dx.doi.org/10.1142/s0218001417540155.

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To remove image noise without considering the noise model, a dual-tree wavelet thresholding method (CDOA-DTDWT) is proposed through noise variance optimization. Instead of building a noise model, the proposed approach using the improved chaotic drosophila optimization algorithm (CDOA), to estimate the noise variance, and the estimated noise variance is utilized to modify wavelet coefficients in shrinkage function. To verify the optimization ability of the improved CDOA, the comparisons with basic DOA, GA, PSO and VCS are performed as well. The proposed method is tested to remove addictive nois

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Li, Peilu, Chunguang Xu, Qinxue Pan, Yuren Lu, and Shuangyi Li. "Denoising of LCR Wave Signal of Residual Stress for Rail Surface Based on Lifting Scheme Wavelet Packet Transform." Coatings 11, no. 5 (2021): 496. http://dx.doi.org/10.3390/coatings11050496.

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According to the acousto elastic effect, the residual stress on the surface of the rail can be evaluated by measuring the change in the propagation velocity of ultrasonic waves, such as longitudinal critically refracted (LCR) waves on the surface of the rail. The LCR wave signal is often polluted by a variety of noise sources, coupled with the influence of the poor surface condition of the inspected component, which greatly reduces the detectability and online measurement ability of the LCR wave signal. This paper proposes the application of the lifting scheme wavelet packet transform (LSWPT)

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Grohs, Philipp, Željko Kereta, and Uwe Wiesmann. "A shearlet-based fast thresholded Landweber algorithm for deconvolution." International Journal of Wavelets, Multiresolution and Information Processing 14, no. 05 (2016): 1650032. http://dx.doi.org/10.1142/s0219691316500326.

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Image deconvolution is an important problem, which has seen plenty of progress in the last decades. Due to its ill-posedness, a common approach is to formulate the reconstruction as an optimization problem[Formula: see text] regularized by an additional sparsity-enforcing term. This term is often modeled as an [Formula: see text] norm measured in the domain of a suitable signal transform. The resulting optimization problem can be solved by an iterative approach via Landweber iterations with soft thresholding of the transform coefficients. Previous approaches focused on thresholding in the wave

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Ergu, Yared Abera. "Medical Image Mixed Denoise using Discrete Multi Wavlet Transform Novel Threshold Method." International Journal of Technology Information and Computer (TIJOTIC) 1, no. 1 (2020): 16–28. https://doi.org/10.5281/zenodo.3888529.

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Generally, most of the<strong> </strong>images are corrupted by noise which is solved by denoising techniques in the image processing.  For that single thresholding techniques are used which removes the additive random noise. The Gaussian -Multi Wavelet technique is utilized to denoising the Gaussian noise present in the mammogram image which is an efficient method due to the capability to acquire the signal energies in few transforms value. In order to enhance and the noise present in the digital mammographics image, the novel Multi Wavelet techniques are used in this paper.  In the

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Yu, Zhou, George A. McMechan, Phil D. Anno, and John F. Ferguson. "Wavelet‐transform‐based prestack multiscale Kirchhoff migration." GEOPHYSICS 69, no. 6 (2004): 1505–12. http://dx.doi.org/10.1190/1.1836823.

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We propose a Kirchhoff‐style algorithm that migrates coefficients obtained by wavelet decomposition of seismic traces over time. Wavelet‐based prestack multiscale Kirchhoff migration involves four steps: wavelet decomposition of the seismic data, thresholding of the resulting wavelet coefficients, multiscale Kirchhoff migration, and image reconstruction from the multiscale images. The migration procedure applied to each wavelet scale is the same as conventional Kirchhoff migration but operates on wavelet coefficients. Since only the wavelet coefficients are migrated, the cost of wavelet‐based

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Kumar, Sushil. "Image De-noising using Wavelet-Like Transform." International Journal of Advance Research and Innovation 5, no. 1 (2017): 46–50. http://dx.doi.org/10.51976/ijari.511707.

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This paper proposes a comparative study of image denoising method using BlockShrink algorithm between the Wavelet transform (DWT) and the Slantlet transform (SLT). Slantlet transform, which is also a wavelet-like transform and a better candidate for signal compression compared to the DWT based scheme and which can provide better time localization. BlockShrink is found to be a better method than other conventional image denoising methods. Through experimental results, it is found that DWT based BlockShrink thresholding is better option than BiShrink thresholding in terms of PSNR for the Gaussia

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Kidsumran, Varakorn, and Werapon Chiracharit. "Mammogram Enhancement Using Wavelet Transform and Sigmoid Function." Applied Mechanics and Materials 781 (August 2015): 632–36. http://dx.doi.org/10.4028/www.scientific.net/amm.781.632.

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Mammogram enhancement is important for the radiologist to diagnose and screen breast cancer. This paper proposes a method to improve contrast and denoising in mammogram using wavelet transform and sigmoid function. First, mammogram is decomposed using wavelet transform and detail coefficients are decreased in order to remove noises by soft thresholding. Inverse wavelet transform is then applied to obtain the denoised image. Finally, sigmoid function is applied to the image to enhance mammogram. Experimental results illustrate that the proposed method can improve contrast and denoise mammogram

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Koranga, Pushpa, Garima Singh, Dikendra Verma, Shshank Chaube, Anuj Kumar, and Sangeeta Pant. "Image Denoising Based on Wavelet Transform using Visu Thresholding Technique." International Journal of Mathematical, Engineering and Management Sciences 3, no. 4 (2018): 444–49. http://dx.doi.org/10.33889/ijmems.2018.3.4-032.

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The image often contains noises due to several factors such as a problem in devices or due to an environmental problem etc. Noise is mainly undesired information, which degrades the quality of the picture. Therefore, image denoising method is adopted to remove the noises from the degraded image which in turn improve the quality of the image. In this paper, image denoising has been done by wavelet transform using Visu thresholding techniques for different wavelet families. PSNR (Peak signal to noise ratio) and RMSE (Root Mean Square Error) value is also calculated for different wavelet families

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Chau, F. T., J. B. Gao, T. M. Shih, and J. Wang. "Compression of Infrared Spectral Data Using the Fast Wavelet Transform Method." Applied Spectroscopy 51, no. 5 (1997): 649–59. http://dx.doi.org/10.1366/0003702971941052.

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The wavelet transform method was used to compress experimental infrared spectra. The methodology utilized was based upon the fast wavelet transform method coupled with the multiresolution signal decomposition, as well as the optimal bit allocation quantization and Huffman coding techniques. The proposed algorithm was applied to reduce the storage space of infrared spectra of several chemicals. The results obtained were compared with those from the fast Fourier transform and the wavelet-based thresholding methods. It was found that the proposed method outperforms the other two.

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Liu, Zhaohua, Yang Mi, and Yuliang Mao. "Improved Real-time Denoising Method Based on Lifting Wavelet Transform." Measurement Science Review 14, no. 3 (2014): 152–59. http://dx.doi.org/10.2478/msr-2014-0020.

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Abstract Signal denoising can not only enhance the signal to noise ratio (SNR) but also reduce the effect of noise. In order to satisfy the requirements of real-time signal denoising, an improved semisoft shrinkage real-time denoising method based on lifting wavelet transform was proposed. The moving data window technology realizes the real-time wavelet denoising, which employs wavelet transform based on lifting scheme to reduce computational complexity. Also hyperbolic threshold function and recursive threshold computing can ensure the dynamic characteristics of the system, in addition, it ca

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Gilda, Sankalp, and Zachary Slepian. "Automatic Kalman-filter-based wavelet shrinkage denoising of 1D stellar spectra." Monthly Notices of the Royal Astronomical Society 490, no. 4 (2019): 5249–69. http://dx.doi.org/10.1093/mnras/stz2577.

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ABSTRACT We propose a non-parametric method to denoise 1D stellar spectra based on wavelet shrinkage followed by adaptive Kalman thresholding. Wavelet shrinkage denoising involves applying the discrete wavelet transform (DWT) to the input signal, ‘shrinking’ certain frequency components in the transform domain, and then applying inverse DWT to the reduced components. The performance of this procedure is influenced by the choice of base wavelet, the number of decomposition levels, and the thresholding function. Typically, these parameters are chosen by ‘trial and error’, which can be strongly d

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Il, Kim Kyong, Ri Ui Hwan, and Chon Bong Pil. "An appropriate thresholding method of wavelet denoising for dropping ambient noise." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 03 (2018): 1850012. http://dx.doi.org/10.1142/s0219691318500121.

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For the non-stationary signal denoising, an effective method for dropping ambient noise is based on discrete wavelet transform. Also, in order to minimize the loss of useful signal and get high SNR in the wavelet denoising, it is very important that the thresholding is suitable for the characteristics of signal. In this paper, we propose new thresholding method to reduce an ambient noise and to detect effectively the useful signal. First, we analyze four kinds of previous wavelet threshold functions (Hard, Soft, Garrote and Hyperbola) and propose new wavelet threshold function compromised betw

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Sandar, Oo. "Study on Speech Compression and Decompression by using Discrete Wavelet Transform." International Journal of Trend in Scientific Research and Development 3, no. 3 (2019): 252–58. https://doi.org/10.31142/ijtsrd21727.

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Speech signal can be compressed and decompressed by discrete wavelet transform technique. Discrete wavelet transform compression is based on compressing speech signal by removing redundancies present in it. Speech compression is a technique to transform speech signal into compact form. Objective of compressing speech signal is to enhance transmission and storage capacity. The compression parameters in speech such as Signal to Noise Ratio SNR , Peak Signal to Noise Ratio PSNR , Normalized Root Mean Square Error NRMSE , Compression Factor CF and Retained Signal Energy RSE are measured using Matl

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BACCHELLI, SILVIA, and SERENA PAPI. "A NOTE ON A MATRIX APPROACH TO MULTIWAVELET APPLICATIONS." International Journal of Wavelets, Multiresolution and Information Processing 04, no. 03 (2006): 509–22. http://dx.doi.org/10.1142/s0219691306001415.

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In recent years, many papers have been devoted to the topic of balanced multiwavelets, namely, multiwavelet bases which are especially designed to avoid the prefiltering step in the implementation of the multiwavelet transform. In this work, we give a simple algebraic proof of how scalar wavelets can be reinterpreted as the most natural balanced multiwavelets, which maintain the good properties of the wavelet bases they come from. We then show how these new bases can be successfully used to apply matrix thresholding for the denoising of images corrupted by Gaussian noise. In fact, this new app

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Ahmed Abdulmunem Hussein and Mohammed Khawwam Ahmed. "Aluminum-Copper alloys, Corrosion, acidic medium, alkali medium." Tikrit Journal of Pure Science 23, no. 2 (2023): 129–36. http://dx.doi.org/10.25130/tjps.v23i2.661.

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This paper suggested a de-noising algorithm used in grayscale images. As long as the noisy image does not give the desired view of its features, de-noising is required. The algorithm is based on block matching and wavelet transformation. Euclidean distance for blocks similarity is exploited, which demonstrate more accurate in finding similar blocks depending on soft thresholding. Regarding wavelet transform, a combine of hard thresholding is performed for HH and LH sub-bands while soft thresholding is used in LL and HL sub-bands of the decomposed images. Three types of noise is encountered: Ga

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Han Chengshan, 韩诚山, 李祥之 Li Xiangzhi, 赵庆磊 Zhao Qinglei, 黄良 Huang Liang, 姜肖楠 Jiang Xiaonan, and 文明 Wen Ming. "Wavefront reconstruction algorithm based on thresholding wavelet transform." High Power Laser and Particle Beams 23, no. 5 (2011): 1197–200. http://dx.doi.org/10.3788/hplpb20112305.1197.

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Downie, T. R., and B. W. Silverman. "The discrete multiple wavelet transform and thresholding methods." IEEE Transactions on Signal Processing 46, no. 9 (1998): 2558–61. http://dx.doi.org/10.1109/78.709546.

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Houamed, Ibtissem, Lamir Saidi, and Fawzi Srairi. "ECG signal denoising by fractional wavelet transform thresholding." Research on Biomedical Engineering 36, no. 3 (2020): 349–60. http://dx.doi.org/10.1007/s42600-020-00075-7.

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MENCATTINI, ARIANNA, MARCELLO SALMERI, FEDERICA CASELLI, BERARDINO SCIUNZI, and ROBERTO LOJACONO. "SUBBAND VARIANCE COMPUTATION OF HOMOSCEDASTIC ADDITIVE NOISE IN DISCRETE DYADIC WAVELET TRANSFORM." International Journal of Wavelets, Multiresolution and Information Processing 06, no. 06 (2008): 895–906. http://dx.doi.org/10.1142/s0219691308002665.

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The paper deals with noise power variation that occurs when Discrete Dyadic Wavelet Transform (DDWT) is applied to signals affected by Wide Sense Stationary (WSS) additive white noise owing to the use of a non orthonormal expansion. An exact relationship between the noise variance in the original signal and the noise variance in the wavelet coefficients at a generic level is derived. This relationship is crucial in the application of wavelet thresholding for signal denoising to properly select the threshold in each subband.

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MASUDA, Arata, Shizuo YAMAMOTO, and Akira SONE. "Deconvolution of Time Series Using Wavelet Transform. Introduction of Wavelet Packets with Soft-Thresholding and Generation of Optimized Wavelets." JSME International Journal Series C 42, no. 1 (1999): 188–94. http://dx.doi.org/10.1299/jsmec.42.188.

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MASUDA, Arata, Shizuo YAMAMOTO, and Akira SONE. "Deconvolution of Time Series Using Wavelet Transform. Introduction of Wavelet Packets with Soft-Thresholding and Generation of Optimized Wavelets." Transactions of the Japan Society of Mechanical Engineers Series C 64, no. 621 (1998): 1580–87. http://dx.doi.org/10.1299/kikaic.64.1580.

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Bitenc, M., D. S. Kieffer, and K. Khoshelham. "EVALUATION OF WAVELET DENOISING METHODS FOR SMALL-SCALE JOINT ROUGHNESS ESTIMATION USING TERRESTRIAL LASER SCANNING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W5 (August 19, 2015): 81–88. http://dx.doi.org/10.5194/isprsannals-ii-3-w5-81-2015.

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The precision of Terrestrial Laser Scanning (TLS) data depends mainly on the inherent random range error, which hinders extraction of small details from TLS measurements. New post processing algorithms have been developed that reduce or eliminate the noise and therefore enable modelling details at a smaller scale than one would traditionally expect. The aim of this research is to find the optimum denoising method such that the corrected TLS data provides a reliable estimation of small-scale rock joint roughness. Two wavelet-based denoising methods are considered, namely Discrete Wavelet Transf

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Setiawan, Iwan, Rachmat Hidayat, Abdul Mahatir Najar, Agus Indra Jaya, and Didi Rosiyadi. "Low-dose computed tomography image denoising using graph wavelet transform with optimal base." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 1696. https://doi.org/10.11591/ijece.v15i2.pp1696-1708.

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Noise in electronic components of computed tomography (CT) detectors behaves like a virus that infects visual quality of CT scans and might distort clinical diagnosis. Modern CT detector technology incorporates high-quality electronic components in conjunction with signal and image processing to ensure optimal image quality while retaining benign doses of x-rays. In this study, a new strategy in signal and image processing directions is proposed by finding the most optimal wavelet base for denoising low-dose CT scan data. The process begins by selecting the appropriate wavelet bases for CT ima

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