Relevant bibliographies by topics / Wavelet Transform Denoising

Contents

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

Academic literature on the topic 'Wavelet Transform Denoising'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Wavelet Transform Denoising"

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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 computing wavelet transforms with different parameters. After the filtering of each detail coefficient, the corresponding wavelet transforms are inverted and the estimated image, having a higher PSNR, is extracted. To benchmark against the best possible denoising method three thresholding techniques have been compared. In this paper we have presented a more practical, implementation oriented work.
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Hamdi, Med. "A Comparative Study in Wavelets, Curvelets and Contourlets as Denoising biomedical Images." Image Processing & Communications 16, no. 3-4 (2011): 13–20. http://dx.doi.org/10.2478/v10248-012-0007-1.

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A Comparative Study in Wavelets, Curvelets and Contourlets as Denoising biomedical ImagesA special member of the emerging family of multi scale geometric transforms is the contourlet transform which was developed in the last few years in an attempt to overcome inherent limitations of traditional multistage representations such as curvelets and wavelets. The biomedical images were denoised using firstly wavelet than curvelets and finally contourlets transform and results are presented in this paper. It has been found that the contourlets transform outperforms the curvelets and wavelet transform in terms of signal noise ratio
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Yin, Ming, Wei Liu, Jun Shui, and Jiangmin Wu. "Quaternion Wavelet Analysis and Application in Image Denoising." Mathematical Problems in Engineering 2012 (2012): 1–21. http://dx.doi.org/10.1155/2012/493976.

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The quaternion wavelet transform is a new multiscale analysis tool. Firstly, this paper studies the standard orthogonal basis of scale space and wavelet space of quaternion wavelet transform in spatialL2(R2), proves and presents quaternion wavelet’s scale basis function and wavelet basis function concepts in spatial scale spaceL2(R2;H), and studies quaternion wavelet transform structure. Finally, the quaternion wavelet transform is applied to image denoising, and generalized Gauss distribution is used to model QWT coefficients’ magnitude distribution, under the Bayesian theory framework, to recover the original coefficients from the noisy wavelet coefficients, and so as to achieve the aim of denoising. Experimental results show that our method is not only better than many of the current denoising methods in the peak signal to noise ratio (PSNR), but also obtained better visual effect.
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Naimi, Hilal, Amelbahahouda Adamou-Mitiche, and Lahcène Mitiche. "Lifting dual tree complex wavelets transform." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (2021): 4008. http://dx.doi.org/10.11591/ijece.v11i5.pp4008-4015.

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We describe the lifting dual tree complex wavelet transform (LDTCWT), a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). We describe a way to estimate the accuracy of this approximation and style appropriate filters to attain this. These benefits are often exploited among applications like denoising, segmentation, image fusion and compression. The results of applications shrinkage denoising demonstrate objective and subjective enhancements over the dual tree complex wavelet transform (DTCWT). The results of the shrinkage denoising example application indicate empirical and subjective enhancements over the DTCWT. The new transform with the DTCWT provide a trade-off between denoising computational competence of performance, and memory necessities. We tend to use the PSNR (peak signal to noise ratio) alongside the structural similarity index measure (SSIM) and the SSIM map to estimate denoised image quality.
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Xiao, Xue Mei. "Comparison and Improvements of Image Denoising Based on Wavelet Transform." Applied Mechanics and Materials 740 (March 2015): 644–47. http://dx.doi.org/10.4028/www.scientific.net/amm.740.644.

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Wavelet transform denoising is an important application of wavelet analysis in signal and image processing. Several popular wavelet denoising methods are introduced including the Mallat forced denoising, the wavelet transform modulus maxima method and the nonlinear wavelet threshold denoising method. Their advantages and disadvantages are compared, which may be helpful in selecting the wavelet denoising methods. At the same time, several improvement methods are offered.
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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 suitable for image corrupted with only additive white Gaussian noise. In the present work, we have proposed a denoising algorithm for medical images based on the lifting-scheme and linear phase characteristic of biorthogonal wavelet transform. A level-dependent soft thresholding function has been used which is based on the standard deviation, the absolute mean and the absolute median of the wavelet coefficients. The linear phase characteristic of the biorthogonal filters used in denoising reduces the distortions at edge points of image. Also, the lifting schemes of the biorthogonal wavelet filters make the algorithm efficient and applicable in real time. Experimental results show that the proposed denoising method outperform standard wavelet, complex wavelet and curvelet-based denoising techniques in terms of the SNR and PSNR (in dB) and it offers effective noise removal from noisy medical images while maintaining sharpness of objects in the image.
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., Sameer Khedkar. "IMAGE DENOISING USING WAVELET TRANSFORM." International Journal of Research in Engineering and Technology 05, no. 04 (2016): 206–12. http://dx.doi.org/10.15623/ijret.2016.0504040.

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Liang, Shu Lai, Zhou Guo Hou, and Zhao Peng Li. "Wavelet Denoising Method for RF Signal." Advanced Materials Research 955-959 (June 2014): 911–15. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.911.

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To reduce the noises in received RF signal and improve the performance of RFID system, a novelty denoising method of wavelet transform in RFID system was proposed in this paper. It analyzed wavelet transform method and FFT method,then introduced wavelet transform principle and wavelet coefficients threshold denoising method. According to the standard ISO18000-3,the RF signal at 13.56MHz was simulated using modulation toolbox in LabVIEW environment. At last, the RF signal with Gauss white noise together, was denoised by wavelet transform method and FFT method respectively. Simulation results showed that the wavelet transform denoising method has higher accuracy, better effect, compared with traditional FFT denoising method. The research on application of wavelet analysis in RF signal denoising is significant and practical. The wavelet analysis is applied to the field of virtual instrument, which can provide people with more accurate and convenient testing, and has good application prospects in engineering applications.
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KHARE, ASHISH, and UMA SHANKER TIWARY. "DAUBECHIES COMPLEX WAVELET TRANSFORM BASED TECHNIQUE FOR DENOISING OF MEDICAL IMAGES." International Journal of Image and Graphics 07, no. 04 (2007): 663–87. http://dx.doi.org/10.1142/s0219467807002854.

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Wavelet based denoising is an effective way to improve the quality of images. Various methods have been proposed for denoising using real-valued wavelet transform. Complex valued wavelets exist but are rarely used. The complex wavelet transform provides phase information and it is shift invariant in nature. In medical image denoising, both removal of phase incoherency as well as maintaining the phase coherency are needed. This paper is an attempt to explore and apply the complex Daubechies wavelet transform for medical image denoising. We have proposed a method to compute a complex threshold, which does not depend on any assumed model of noise. In this sense this is a "universal" method. The proposed complex-domain shrinkage function depends on mean, variance and median of wavelet coefficients. To test the effectiveness of the proposed method, we have computed the input and output SNR and PSNR of various types of medical images. The method gives an improvement for Gaussian additive, Speckle and Salt-&-Pepper noise as well as for the mixture of these noise types for a range of noisy images with 15 db to 30 db noise levels and outperforms other real-valued wavelet transform based methods. The application of the proposed method to Ultrasound, X-ray and MRI images is demonstrated in the experiments.
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He, Shuang Shuang, Yuan Yuan Jiang, and Jin Yan Zheng. "A Novel Image Denoising Method in 2-D Fractional Time-Frequency Domain." Applied Mechanics and Materials 734 (February 2015): 586–89. http://dx.doi.org/10.4028/www.scientific.net/amm.734.586.

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To improve image quality and a higher level of follow-up image process needed, it's of great importance to do the image denoising process first. A new image denoising method in two-dimensional (2-D) fractional time-frequency domain is proposed in this paper. Through the realization of 2-D fractional wavelet transform algorithm, the 2-D fractional wavelet transform theory is applied to image denoising, and compare with image denoising method based on 2-D wavelet transform. A large number of image denoising simulation studies have shown that, the Peak Signal to Noise Ratio of output images based on the proposed method can be effectively improved, and preserve detail information effectively and reduce the noise at the same time. It proved 2-D fractional wavelet transform is a new and effective time-frequency domain image denoising method.
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Dissertations / Theses on the topic "Wavelet Transform Denoising"

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Savka, Andriy. "Wavelet Transform in Financial Time Series Analysis: Denoising and Forecast." Kent State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=kent1543573160243739.

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Al, Marzouqi Hasan. "Curvelet transform with adaptive tiling." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52961.

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In this dissertation we address the problem of adapting frequency domain tiling using the curvelet transform as the basis algorithm. The optimal tiling, for a given class of images, is computed using denoising performance as the cost function. The major adaptations considered are: the number of scale decompositions, angular decompositions per scale/quadrant, and scale locations. A global optimization algorithm combining the three adaptations is proposed. Denoising performance of adaptive curvelets is tested on seismic and face data sets. The developed adaptation procedure is applied to a number of different application areas. Adaptive curvelets are used to solve the problem of sparse data recovery from subsampled measurements. Performance comparison with default curvelets demonstrates the effectiveness of the adaptation scheme. Adaptive curvelets are also used in the development of a novel image similarity index. The developed measure succeeds in retrieving correct matches from a variety of textured materials. Furthermore, we present an algorithm for classifying different types of seismic activities.
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Balster, Eric J. "Video compression and rate control methods based on the wavelet transform." Columbus, Ohio : Ohio State University, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1086098540.

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Thesis (Ph. D.)--Ohio State University, 2003.<br>Title from first page of PDF file. Document formatted into pages; contains xxv, 142 p.; also includes graphics. Includes abstract and vita. Advisor: Yuan F. Zheng, Dept. of Electrical and Computer Engineering. Includes bibliographical references (p. 135-142).
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Cheng, Wei. "Studies on NDT Image Denoising by Wavelet Transform and Self-Orgnizing Maps." 京都大学 (Kyoto University), 2004. http://hdl.handle.net/2433/147636.

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Elahi, Pegah. "Application of Noise Invalidation Denoising in MRI." Thesis, Linköpings universitet, Medicinsk informatik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-85215.

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Magnetic Resonance Imaging (MRI) is a common medical imaging tool that have beenused in clinical industry for diagnostic and research purposes. These images are subjectto noises while capturing the data that can eect the image quality and diagnostics.Therefore, improving the quality of the generated images from both resolution andsignal to noise ratio (SNR) perspective is critical. Wavelet based denoising technique isone of the common tools to remove the noise in the MRI images. The noise is eliminatedfrom the detailed coecients of the signal in the wavelet domain. This can be done byapplying thresholding methods. The main task here is to nd an optimal threshold andkeep all the coecients larger than this threshold as the noiseless ones. Noise InvalidationDenoising technique is a method in which the optimal threshold is found by comparingthe noisy signal to a noise signature (function of noise statistics). The original NIDeapproach is developed for one dimensional signals with additive Gaussian noise. In thiswork, the existing NIDe approach has been generalized for applications in MRI imageswith dierent noise distribution. The developed algorithm was tested on simulated datafrom the Brainweb database and compared with the well-known Non Local Mean lteringmethod for MRI. The results indicated better detailed structural preserving forthe NIDe approach on the magnitude data while the signal to noise ratio is compatible.The algorithm shows an important advantageous which is less computational complexitythan the NLM method. On the other hand, the Unbiased NLM technique is combinedwith the proposed technique, it can yield the same structural similarity while the signalto noise ratio is improved.
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Liu, Chunyu. "VIRTUALIZED CLOUD PLATFORM MANAGEMENT USING A COMBINED NEURAL NETWORK AND WAVELET TRANSFORM STRATEGY." CSUSB ScholarWorks, 2018. https://scholarworks.lib.csusb.edu/etd/615.

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This study focuses on implementing a log analysis strategy that combines a neural network algorithm and wavelet transform. Wavelet transform allows us to extract the important hidden information and features of the original time series log data and offers a precise framework for the analysis of input information. While neural network algorithm constitutes a powerfulnonlinear function approximation which can provide detection and prediction functions. The combination of the two techniques is based on the idea of using wavelet transform to denoise the log data by decomposing it into a set of coefficients, then feed the denoised data into a neural network. The experimental outputs reveal that this strategy can have a better ability to identify the patterns among problems in a log dataset, and make predictions with a better accuracy. This strategy can help the platform maintainers to adopt corresponding actions to eliminate risks before the occurrence of serious damages.
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Tsai, Shu-Jen Steven. "Power Transformer Partial Discharge (PD) Acoustic Signal Detection using Fiber Sensors and Wavelet Analysis, Modeling, and Simulation." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/35983.

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In this work, we first analyze the behavior of the acoustic wave from the theoretical point of view using a simplified 1-dimensional model. The model was developed based on the conservation of mass, the conservation of momentum, and the state equation; in addition, the fluid medium obeys Stokes assumption and it is homogeneous, adiabatic and isentropic. Experiment and simulation results show consistency to theoretical calculation. The second part of this thesis focuses on the PD signal analysis from an on-site PD measurement of the in-house design fiber optic sensors (by Virginia Tech, Center for Photonics Technology). Several commercial piezoelectric transducers (PZTs) were also used to compare the measurement results. The signal analysis employs the application of wavelet-based denoising technique to remove the noises, which mainly came from vibration, EMI, and light sources, embedded in the PD signal. The denoising technique includes the discrete wavelet transform (DWT) decomposition, thresh-holding of wavelet coefficients, and signal recovery by inverse discrete wavelet transform. Several approaches were compared to determine the optimal mother wavelet. The threshold limits are selected to remove the maximum Gaussian noises for each level of wavelet coefficients. The results indicate that this method could extract the PD spike from the noisy measurement effectively. The frequency of the PD pulse is also analyzed; it is shown that the frequencies lie in the range of 70 kHz to 250 kHz. In addition, with the assumed acoustic wave propagation delay between PD source and sensors, it was found that all PD activities occur in the first and third quadrant in reference to the applied sinusoidal transformer voltage.<br>Master of Science
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Song, Xiaodi. "The automation and optimisation of wavelet transform techniques for PD denoising and pulse shape classification in power plant." Thesis, Glasgow Caledonian University, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.517952.

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Chen, Shuo. "MALDI-TOF MS data processing using wavelets, splines and clustering techniques." [Johnson City, Tenn. : East Tennessee State University], 2004. http://etd-submit.etsu.edu/etd/theses/available/etd-1112104-113123/unrestricted/ChenS121404f.pdf.

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Thesis (M.S.)--East Tennessee State University, 2004.<br>Title from electronic submission form. ETSU ETD database URN: etd-1112104-113123 Includes bibliographical references. Also available via Internet at the UMI web site.
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Zhao, Fangwei. "Multiresolution analysis of ultrasound images of the prostate." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2004. http://theses.library.uwa.edu.au/adt-WU2004.0028.

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[Truncated abstract] Transrectal ultrasound (TRUS) has become the urologist’s primary tool for diagnosing and staging prostate cancer due to its real-time and non-invasive nature, low cost, and minimal discomfort. However, the interpretation of a prostate ultrasound image depends critically on the experience and expertise of a urologist and is still difficult and subjective. To overcome the subjective interpretation and facilitate objective diagnosis, computer aided analysis of ultrasound images of the prostate would be very helpful. Computer aided analysis of images may improve diagnostic accuracy by providing a more reproducible interpretation of the images. This thesis is an attempt to address several key elements of computer aided analysis of ultrasound images of the prostate. Specifically, it addresses the following tasks: 1. modelling B-mode ultrasound image formation and statistical properties; 2. reducing ultrasound speckle; and 3. extracting prostate contour. Speckle refers to the granular appearance that compromises the image quality and resolution in optics, synthetic aperture radar (SAR), and ultrasound. Due to the existence of speckle the appearance of a B-mode ultrasound image does not necessarily relate to the internal structure of the object being scanned. A computer simulation of B-mode ultrasound imaging is presented, which not only provides an insight into the nature of speckle, but also a viable test-bed for any ultrasound speckle reduction methods. Motivated by analysis of the statistical properties of the simulated images, the generalised Fisher-Tippett distribution is empirically proposed to analyse statistical properties of ultrasound images of the prostate. A speckle reduction scheme is then presented, which is based on Mallat and Zhong’s dyadic wavelet transform (MZDWT) and modelling statistical properties of the wavelet coefficients and exploiting their inter-scale correlation. Specifically, the squared modulus of the component wavelet coefficients are modelled as a two-state Gamma mixture. Interscale correlation is exploited by taking the harmonic mean of the posterior probability functions, which are derived from the Gamma mixture. This noise reduction scheme is applied to both simulated and real ultrasound images, and its performance is quite satisfactory in that the important features of the original noise corrupted image are preserved while most of the speckle noise is removed successfully. It is also evaluated both qualitatively and quantitatively by comparing it with median, Wiener, and Lee filters, and the results revealed that it surpasses all these filters. A novel contour extraction scheme (CES), which fuses MZDWT and snakes, is proposed on the basis of multiresolution analysis (MRA). Extraction of the prostate contour is placed in a multi-scale framework provided by MZDWT. Specifically, the external potential functions of the snake are designated as the modulus of the wavelet coefficients at different scales, and thus are “switchable”. Such a multi-scale snake, which deforms and migrates from coarse to fine scales, eventually extracts the contour of the prostate
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Books on the topic "Wavelet Transform Denoising"

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Shukla, K. K. Efficient Algorithms for Discrete Wavelet Transform: With Applications to Denoising and Fuzzy Inference Systems. Springer London, 2013.

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Pham, Tuan Van. Wavelet analysis for robust speech processing and applications: Applications of discrete wavelet transform and wavelet denoising to speech enhancement and robust speech recognition. VDM, Verlag Dr. Müller, 2008.

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Wendling, Fabrice, Marco Congendo, and Fernando H. Lopes da Silva. EEG Analysis. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0044.

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This chapter addresses the analysis and quantification of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Topics include characteristics of these signals and practical issues such as sampling, filtering, and artifact rejection. Basic concepts of analysis in time and frequency domains are presented, with attention to non-stationary signals focusing on time-frequency signal decomposition, analytic signal and Hilbert transform, wavelet transform, matching pursuit, blind source separation and independent component analysis, canonical correlation analysis, and empirical model decomposition. The behavior of these methods in denoising EEG signals is illustrated. Concepts of functional and effective connectivity are developed with emphasis on methods to estimate causality and phase and time delays using linear and nonlinear methods. Attention is given to Granger causality and methods inspired by this concept. A concrete example is provided to show how information processing methods can be combined in the detection and classification of transient events in EEG/MEG signals.
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Book chapters on the topic "Wavelet Transform Denoising"

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Shukla, K. K., and Arvind K. Tiwari. "PVM Implementation of DWT-Based Image Denoising." In Efficient Algorithms for Discrete Wavelet Transform. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4941-5_4.

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Li, Ying, Shengwei Zhang, and Jie Hu. "Combining Curvelet Transform and Wavelet Transform for Image Denoising." In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14932-0_40.

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Nandal, Savita, and Sanjeev Kumar. "Image Denoising Using Fractional Quaternion Wavelet Transform." In Proceedings of 2nd International Conference on Computer Vision & Image Processing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7898-9_25.

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Vetrivelan, P., and A. Kandaswamy. "Medical Image Denoising Using Wavelet-Based Ridgelet Transform." In Lecture Notes in Electrical Engineering. Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1157-0_25.

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Ukil, Abhisek. "Practical Denoising of MEG Data Using Wavelet Transform." In Neural Information Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893257_65.

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Guang-hui, Li, and Zhang Xing-hui. "A Pulse Denoising Method Based on Wavelet Transform." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-03718-4_140.

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Zhu, Hua, and Xiaomei Wang. "Image Denoising by Wavelet Transform Based on New Threshold." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4572-0_30.

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Gómez, Alejandro, Juan P. Ugarte, and Diego Mauricio Murillo Gómez. "Bioacoustic Signals Denoising Using the Undecimated Discrete Wavelet Transform." In Communications in Computer and Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00353-1_27.

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Amala Nair, C., and R. Lavanya. "Enhanced Empirical Wavelet Transform for Denoising of Fundus Images." In Soft Computing Systems. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1936-5_13.

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Gopi, Varun P., P. Palanisamy, and S. Issac Niwas. "Capsule Endoscopic Colour Image Denoising Using Complex Wavelet Transform." In Wireless Networks and Computational Intelligence. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31686-9_26.

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Conference papers on the topic "Wavelet Transform Denoising"

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Ruikar, Sachin, and D. D. Doye. "Image denoising using wavelet transform." In 2010 2nd International Conference on Mechanical and Electrical Technology (ICMET). IEEE, 2010. http://dx.doi.org/10.1109/icmet.2010.5598411.

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Jun-Hai Zhai and Su-Fang Zhang. "Image denoising via wavelet threshold: single wavelet and multiple wavelets transform." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527500.

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Dautov, Cigdem Polat, and Mehmet Sirac Ozerdem. "Wavelet transform and signal denoising using Wavelet method." In 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404418.

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Gupta, Vikas, Rajesh Mahle, and Raviprakash S. Shriwas. "Image denoising using wavelet transform method." In 2013 Tenth International Conference on Wireless and Optical Communications Networks - (WOCN). IEEE, 2013. http://dx.doi.org/10.1109/wocn.2013.6616235.

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Zou, Binyi, Hui Liu, Zhenhong Shang, and Ruixin Li. "Image denoising based on wavelet transform." In 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2015. http://dx.doi.org/10.1109/icsess.2015.7339070.

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Ha, Yan. "Image denoising based on wavelet transform." In 4th International Symposium on Advanced Optical Manufacturing and testing technologies: Optical Test and Measurement Technology and Equipment, edited by Yudong Zhang, James C. Wyant, Robert A. Smythe, and Hexin Wang. SPIE, 2009. http://dx.doi.org/10.1117/12.828818.

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Wang, Tiedong, Wenqing Liu, Yujun Zhang, Min Wang, Xiaomei Wang, and Min Xu. "Medicine image denoising using wavelet transform." In Fourth International Conference on Photonics and Imaging in Biology and Medicine, edited by Kexin Xu, Qingming Luo, Da Xing, Alexander V. Priezzhev, and Valery V. Tuchin. SPIE, 2006. http://dx.doi.org/10.1117/12.710902.

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Bakhtazad, Amid, and Jose A. Romagnoli. "Coefficient denoising method with wavelet transform." In SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, edited by Michael A. Unser, Akram Aldroubi, and Andrew F. Laine. SPIE, 1999. http://dx.doi.org/10.1117/12.366831.

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Shao-Wei Dai, Yan-Kui Sun, Xiao-Lin Tian, and Ze-Sheng Tang. "Image denoising based on complex contourlet transform." In 2007 International Conference on Wavelet Analysis and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/icwapr.2007.4421735.

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GARVANOV, Ivan, Ruska IYINBOR, Magdalena GARVANOVA, and Nikolay GESHEV. "Denoising of Pulsar Signal Using Wavelet Transform." In 2019 16th Conference on Electrical Machines, Drives and Power Systems (ELMA). IEEE, 2019. http://dx.doi.org/10.1109/elma.2019.8771495.

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