Relevant bibliographies by topics / De-noising

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'De-noising'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "De-noising"

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Li, Xin, Xue Jun Li, and Guang Bin Wang. "De-Noising Method of Acoustic Emission Signal for Rolling Bearing Based on Adaptive Wavelet Correlation Analysis." Applied Mechanics and Materials 273 (January 2013): 188–92. http://dx.doi.org/10.4028/www.scientific.net/amm.273.188.

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In acoustic emission (AE) detection technique, to avoid the serious noise disturbance in the fault diagnosis of rotary machine, a de-noising method based on adaptive wavelet correlation analysis to be applied to the AE signal is proposed. First, AE signals are decomposed by dyadic wavelet transform and at the same time the AE signal is divided into available coefficients and noise coefficients. Secondly, the available coefficients are reconstructed to restore the original real signal after de-noising process. Finally, the de-noising threshold is set by adaptive threshold method based on wavelet entropy. On the simulation of AE signal and the bearing fault measured AE signal using wavelet entropy correlation de-noising method, the traditional wavelet de-noising method and the traditional lifting wavelet de-noising method three kinds of de-noising methods are compared, the results show that the wavelet entropy correlation de-noising method can greatly improve the rolling bearing AE signal de-noising effect.
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Zeng, Jin Xia, Guo Fu Wang, Fa Quan Zhang, and Jin Cai Ye. "The De-Noising Algorithm Based on Intrinsic Time-Scale Decomposition." Advanced Materials Research 422 (December 2011): 347–52. http://dx.doi.org/10.4028/www.scientific.net/amr.422.347.

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A new de-noising algorithm based on Intrinsic Time-scale Decomposition (ITD) is proposed after analyzing the statistical characteristics of additional Gaussian white noise decomposed by ITD. Compared with the Wavelet Threshold De-noising(WTD) and de-noising algorithms based on empirical mode decomposition (EMD), the numerical simulation results show that this algorithm has comparable performance with the de-noising based on EMD and the WTD, and it is no need for spline interpolation, iterative sifting and selection of the wavelet base. It is a new adaptive de-noising algorithm.
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Liu, Yan Xia, Bing Han, Yan Li, Bei Bei Dong, and Yan Yan Cao. "Ultrasonic Image De-Noising Based on New Wavelet Threshold Function." Applied Mechanics and Materials 325-326 (June 2013): 1641–44. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1641.

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In order to improve the quality of image de-noising,against the disadvantages of the distortion caused by the method of the hard threshold de-noising and the fuzzy phenomenon of the details caused by the method of the soft threshold de-noising, this article proposes a new method of wavelet threshold de-noising for the ultrasonic images. It is indicated in the simulation result that this method has good de-noising function: it can remove the noise effectively and retain the details of the images and the edge information at the same time.
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Gan, Lu, Long Zhou, and Shan Mei Liu. "A De-Noising Method for GPR Signal Based on EEMD." Applied Mechanics and Materials 687-691 (November 2014): 3909–13. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3909.

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Aiming at the de-noising of GPR echo signal, a de-noising method based on EEMD and wavelet is presented. First the echo signal data is processed with EEMD and yields IMF components. Then the IMF components which indicate noise are subtracted. Next, the high frequency IMF components of the remaining are subjected to wavelet threshold. Finally, the signal is reconstructed using the de-noising IMF and low frequency IMF to realize signal de-noising. Compared with other commonly used methods, EEMD-wavelet method has improvement on SNR. The experiment results show its effectiveness and feasibility in GPR de-noising.
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Prabakaran, M. P., A. Sivasubramanian, A. Jawahar, and K. Chitra. "Performance Analysis of Wavelet Packet Transform Based De-Noising Receiver for Visible Light Communication by Using Single Source." International Journal of Engineering Research in Africa 20 (October 2015): 195–201. http://dx.doi.org/10.4028/www.scientific.net/jera.20.195.

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In this paper, wavelet packet transform (WPT) based de-noising receiver for visible-light communication (VLC) using a white light-emitting diode (LED) is studied for indoor applications such as short distance wireless connectivity, optical wireless local area network, and optical wireless input / output control devise (remote control). Previously, reported discrete wavelet transform based de-noising for indoor optical wireless communication; here we considered wavelet packet transform based de-noising technique. The process starts with the evaluation of the performance of de-noising receiver by calculating the received optical power, signal noise ratio (SNR), path loss and bit error rate (BER). Throughout the simulation results, the SNR performance is inversely proportional to the distance. Analytical study of SNR for VLC system without de-noising for indoor applications has been studied. In this paper de-noising technique is considered for reduction of noise. The DWPT based de-noising receiver, with a single source improves the SNR performance approximately by 2% compared to the one without de-noising receiver.
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Bi, Zhou Yang, Jian Hui Chen, Wen Jie Ju, Ming Wang, and Ji Chen Li. "Method of Ultrasonic Signal De-Noising Based on Lifting Wavelet Improved Threshold." Applied Mechanics and Materials 513-517 (February 2014): 3818–21. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3818.

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The article established the mathematical model of ultrasonic flaw echo signals. First, the basic theory of wavelet transform is introduced, the principle of the wavelet threshold de-noising method is analyzed; Then on the basis of soft and hard threshold function, the paper proposes a method based on lifting wavelet de-noising. And from two aspects of signal-to-noise ratio (SNR) and mean square error (MSE) the de-noising performance is analysed. The results show that the method improved the shortcomings of soft and hard threshold de-noising method, and got a better de-noising performance and higher signal-to-noise ratio. So in real-time signal de-noising aspect the lifting wavelet has a very good application prospect.
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Ni, Wei Chuan, Bai Hui Zhu, and Zhi Ping Wan. "A De-Nosing Algorithm of Wavelet Threshold Based on Efficient Threshold Function." Advanced Materials Research 709 (June 2013): 624–27. http://dx.doi.org/10.4028/www.scientific.net/amr.709.624.

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Image digitization and transmission process often subject to outside interference that was easy to let the image turn into de-noising image; General de-noising was easy made the image details blurred. Against the phenomenon, this paper using "mathematical microscope" said with wavelet transform, and according to the inherent characteristics of the human eye's visual. Put a new optimize the scan mode of the wavelet coefficients, and proposes a new threshold de-noising algorithm. At last, decrease the overhead of unnecessary coding algorithm; simplified scanning path to reduce，decrease encoding time and improve de-noising ability to effect, making the algorithm de-noising while protecting image details; Tests showed that the research to achieve the purpose of the above study and stability de-noising advantage.
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Li, Jian Nan, Lei Yu, and Li Ying Zheng. "Surfacelet Hard-Threshold Video De-Noising Method Combining Multi-Cycle Spinning." Applied Mechanics and Materials 263-266 (December 2012): 202–6. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.202.

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Taking into account that the Surfacelet hard threshold video de-noising would produce pseudo-Gibbs phenomenon, this paper proposes Surfacelet transform video de-noising method combining Cycle Spinning. Experimental results show that video which is processed by this algorithm not only eliminates the pseudo-Gibbs phenomenon which is produced by hard threshold de-noising, but also achieves higher PSNR, significantly improving the video quality after de-noising.
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Li, Jun. "An Image De-Noising Algorithm Based on K-SVD and BM3D." Applied Mechanics and Materials 596 (July 2014): 333–36. http://dx.doi.org/10.4028/www.scientific.net/amm.596.333.

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The existence of noise affects the quality of the image seriously. The image de-noising algorithm based on KSVD appears fuzzy, where weak texture smooth area also can appear false textures, at the same time, when the noise was very big, the de-noising effect would not always be ideal. This paper proposed an image de-noising method based on K-SVD dictionary and BM3D. The algorithm can solve image weak texture fuzzy problems and weak edges effectively. The experimental results show that, compare with K-SVD de-noising algorithm, this algorithm has a good de-noising ability, which keeping the detail and the edge character of the image better.
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Ali, Nawafil Abdulwahab, and Imad Al Shaikhli. "Proposed De-noising Algorithm." International Journal on Perceptive and Cognitive Computing 6, no. 2 (December 14, 2020): 90–96. http://dx.doi.org/10.31436/ijpcc.v6i2.164.

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minimizing noises from images to restore it and increase its quality is a crucial step. For this, an efficient algorithms were proposed to remove noises such as (salt pepper, Gaussian, and speckle) noises from grayscale images. The algorithm did that by selecting a window measuring 3x3 as the center of processing pixels, other algorithms did that by using median filter (MF), adopted median filter (AMF), adopted weighted filter (AWF), and the adopted weighted median filter (AWMF). The results showed that the proposed algorithm compares to previous algorithms by having a better signal-to-noise ratio (PSNR).
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Dissertations / Theses on the topic "De-noising"

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Schwartz, David, and David Schwartz. "Navigational Neural Coding and De-noising." Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/625322.

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The work discussed in this thesis is the product of investigation on information and coding theoretic properties of colluding populations of navigationally relevant mammalian neurons. For brevity and completeness, that work is presented chronologically in the order in which it was investigated. This thesis details coding theoretic properties of (and develop a model for communication between) colluding populations of spatially responsive neurons in the hippocampus (HC) and medial entorhinal cortex (MEC) through a hypothetical layer of interneurons (each of which posesses exclusively excitatory or inhibitory synapses). This work presents analysis of the changes in network structure induced by an anti-Hebbian learning process and translate these analyses into biologically testable hypotheses. Further, it is demonstrated that for appropriately parameterized codes (i.e. populations of grid and place cells in MEC and HC, respectively), this network is able to learn the code and correct for errors introduced by neural noise, potentially explaining the results of a correlational study: Place cell variability sharply decreases at a time that coincides with the maturation of the grid cell network in developing mice. Further, this work predicts that disruption of the grid cell network (e.g. via optogenetic inactivation and lesioning) should increase the variability of place cell firing, and impair decoding from these place cells' activities. Continuing down this avenue, we consider how the inclusion of a population of the somewhat controversial time cells (purportedly residing in HC and MEC) impacts de-noising network structure, coding properties of the population of populations of all three classes of navigatory neuron, and denoisability. These results are translated to testable neurobiological predictions. Additionally, to ensure realistic stimulus statistics, locations and times are taken from real rat paths recorded from navigating rats in the Computational and Experimental Neuroscience Laboratory at the University of Arizona. Interestingly, while time cells exhibit some of the coding and information theoretic trends described in chapter 4, in certain cases, they admit surprising connectivity trends. Most surprisingly, after including time cells in this framework it was discovered that some classes of neural noise appear to improve decoding accuracy over the entire path while simultaneously impairing accuracy of decoding position and time independently.
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Fike, Amanda(Amanda J. ). "De-noising and de-blurring of images using deep neural networks." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123266.

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Thesis: S.B., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (page 12).
Deep Neural Networks (DNNs) [1] are often used for image reconstruction, but perform better reconstructing the low frequencies of the image than the high frequencies. This is especially the case when using noisy images. In this paper, we test using a Learning Synthesis Deep Neural Network (LS-DNN) [2] in combination with BM3D [3], an off the shelf de-noising tool, to generate images, attempting to decouple the de-noising and de-blurring steps to reconstruct noisy, blurry images. Overall, the LS-DNN performed similarly to the DNN trained only with respect to the ground truth images, and decoupling the de-noising and de-blurring steps underperformed compared to the results of images de-blurred and de-noised simultaneously with a DNN.
by Amanda Fike.
S.B.
S.B. Massachusetts Institute of Technology, Department of Mechanical Engineering
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Chen, Guangyi. "Applications of wavelet transforms in pattern recognition and de-noising." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0006/MQ43552.pdf.

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Khorbotly, Sami. "DESIGN AND IMPLEMENTATION OF LOW COST DE-NOISING SYSTEMS FOR REAL-TIME CONTROL APPLICATIONS." University of Akron / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=akron1180976720.

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Sawant, Rupesh Prakash. "Bio-Particle Counting and Sizing Using Micro-Machined Multichannel Coulter Counter with Wavelet Based De-Noising." University of Akron / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=akron1196456801.

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Shafri, Helmi Zulhaidi Mohd. "An assessment of the potential of wavelet-based de-noising in the analysis of remotely sensed data." Thesis, University of Nottingham, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.397592.

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Vrba, Filip. "Odstranění hluku magnetické rezonance v nahrávkách řeči." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442573.

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This thesis deals with the removal of noise in speech recordings that have been recorded in an MRI environment. For this purpose, the Nvidia RTX Voice technology, the VST plug-in module Noisereduce and a self-designed method of subtractive de-noising of recordings are used. A program with a simple graphical interface in Python is implemented within the work to retrieve the recordings and then de-noise them using the proposed methods. The work includes measurements in a magnetic resonance environment with two microphones. The quality of the processed recordings is tested within the program using the STOI (Short-Time Objective Intelligibility Measure) method as well as the subjective analysis method within listening tests.
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Venter, Nielen Christoff. "The effects of empirical mode decomposition based on de-noising techniques in improving detection of directly stimulated skeletal muscle response." Thesis, University of Cape Town, 2013. http://hdl.handle.net/11427/3213.

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Palaniappan, Prashanth. "De-noising of Real-time Dynamic Magnetic Resonance Images by the Combined Application of Karhunen-Loeve Transform (KLT) and Wavelet Filtering." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1357269157.

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Carter, Drew Davis. "Characterisation of cardiac signals using level crossing representations." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/130760/1/Drew_Carter_Thesis.pdf.

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This study examines a type of event-based sampling known as Level Crossing - its behaviour when applied to noisy signals, and an application to cardiac arrhythmia detection. Using a probabilistic approach, it presents a mathematical description of events sampled from noisy signals, and uses the model to estimate characteristics of the underlying clean signal. It evaluates the use of segments of polynomials, calculated from the Level Crossing samples of real cardiac signals, as features for machine learning algorithms to identify various types of arrhythmia.
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Books on the topic "De-noising"

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Dey, Nilanjan, and Amira S. Ashour. Parametric and Nonparametric Techniques in Medical Image De-noising and Restoration. Academic Press, 2022.

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Dey, Nilanjan, and Amira S. Ashour. Parametric and Nonparametric Techniques in Medical Image de-Noising and Restoration. Elsevier Science & Technology Books, 2022.

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Book chapters on the topic "De-noising"

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Zhang, Yu-Jin. "Image De-Noising." In A Selection of Image Processing Techniques, 25–62. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003241416-2.

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Coifman, R. R., and D. L. Donoho. "Translation-Invariant De-Noising." In Wavelets and Statistics, 125–50. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-2544-7_9.

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Chen, Fu-qiang, Yan Wu, Guo-dong Zhao, Jun-ming Zhang, Ming Zhu, and Jing Bai. "Contractive De-noising Auto-Encoder." In Intelligent Computing Theory, 776–81. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09333-8_84.

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Cohen, Shimon, and Rami Ben-Ari. "Image De-noising by Bayesian Regression." In Image Analysis and Processing – ICIAP 2011, 19–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24085-0_3.

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Alessio, Silvia Maria. "De-noising and Compression by Wavelets." In Signals and Communication Technology, 715–42. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25468-5_15.

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Takahashi, Takashi, and Takio Kurita. "Robust De-noising by Kernel PCA." In Artificial Neural Networks — ICANN 2002, 739–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_120.

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Chen, Lixia, and Zhaoyu Shou. "Improved De-noising Algorithm on Directed Diffusion." In Lecture Notes in Electrical Engineering, 633–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27287-5_103.

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Bruni, V., and D. Vitulano. "Image De-noising via Overlapping Wavelet Atoms." In Lecture Notes in Computer Science, 179–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30125-7_23.

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Weyrich, Norman, and Gregory T. Warhola. "De-Noising Using Wavelets and Cross Validation." In Approximation Theory, Wavelets and Applications, 523–32. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-015-8577-4_36.

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Göknar, Evren. "De-Noising/Audio Restoration (Out, Damned Spot!)." In Major Label Mastering, 153–57. New York : Routledge, 2020.: Focal Press, 2020. http://dx.doi.org/10.4324/9781315164106-22.

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Conference papers on the topic "De-noising"

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Xiao, Qian, Yingchao Li, Shuwei Wu, and Zhipeng Zhao. "Real-time Traffic Data De-noising Based on Wavelet De-noising." In 2016 International Conference on Civil, Transportation and Environment. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/iccte-16.2016.240.

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Oktem, H., K. O. Egiazarian, and J. Nousiainen. "Local adaptive de-noising techniques in transform domain for EMCG de-noising." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.758262.

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Jabbar, Muhammad Usama, Waqar Ahmad, Ali Waqar, Muhammad Jamshed Abbas, and Sunil Pervaiz. "Transformation based image de-noising." In 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). IEEE, 2020. http://dx.doi.org/10.1109/icomet48670.2020.9074064.

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Qingju, Zhang, and Luo Zhizeng. "Wavelet De-Noising of Electromyography." In 2006 International Conference on Mechatronics and Automation. IEEE, 2006. http://dx.doi.org/10.1109/icma.2006.257406.

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Li Su and Guoliang Zhao. "De-Noising of ECG Signal Using Translation- Invariant Wavelet De-Noising Method with Improved Thresholding." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1615845.

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Shi, Xiao-xia, and Jun-zhi Li. "Research on the De-Noising Algorithm." In 2009 2nd International Congress on Image and Signal Processing (CISP). IEEE, 2009. http://dx.doi.org/10.1109/cisp.2009.5304431.

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Wen, Fang, and Tian-ze Li. "Research on CCD De-Noising Technology." In 2009 2nd International Congress on Image and Signal Processing (CISP). IEEE, 2009. http://dx.doi.org/10.1109/cisp.2009.5304489.

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Erturk, M. Arcan, Paul A. Bottomley, and AbdEl-Monem M. El-Sharkawy. "Spectral subtraction de-noising of MRI." In 2012 Cairo International Biomedical Engineering Conference (CIBEC). IEEE, 2012. http://dx.doi.org/10.1109/cibec.2012.6473325.

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Liu, Q., L. G. Han, and C. Q. Tan. "Simultaneously Deblending, De-noising and Interpolation." In 75th EAGE Conference and Exhibition incorporating SPE EUROPEC 2013. Netherlands: EAGE Publications BV, 2013. http://dx.doi.org/10.3997/2214-4609.20130266.

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Sawant, Chitrangi, and Harishchandra T. Patii. "Wavelet based ECG signal de-noising." In 2014 International Conference on Networks & Soft Computing (ICNSC). IEEE, 2014. http://dx.doi.org/10.1109/cnsc.2014.6906684.

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Reports on the topic "De-noising"

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Gyaourova, A., C. Kamath, and I. K. Fodor. Undecimated Wavelet Transforms for Image De-noising. Office of Scientific and Technical Information (OSTI), November 2002. http://dx.doi.org/10.2172/15002085.

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