Relevant bibliographies by topics / Denoising

Contents

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

Academic literature on the topic 'Denoising'

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

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Journal articles on the topic "Denoising"

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Liu, Qing Yi. "Application of Wavelet Analysis in Denoising Seismic Data." Applied Mechanics and Materials 530-531 (February 2014): 540–43. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.540.

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The random noise is the kind of noise with wide frequency band in seismic data detected by the optical acceleration sensors. The noises influence and destroy the useful signal of the seismic information. There are a lot of methods to remove noise and one of the standard methods to remove the noise of the signal was the fast Fourier transform (FFT) which was the linear Fourier smoothing. In this paper, the novel denoising method based on wavelet analysis was introduced. The denoising results of seismic data with the noise with FFT method and wavelet analysis method, respectively. SNRs of the signal with noise, FFT denoisng and wavelet analysis denoising are-8.69, -1.13, and 8.27 respectively. The results show that the wavelet analysis method is prior to the traditional denoising method. The resolution of the seismic data improves.
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PEI, W. C., H. Y. LONG, Y. G. LI, H. C. JI, L. WANG, and S. ZHANG. "APPLICATION OF QUANTITATIVE ANALYSIS OF GEAR FAULT IN μ-SVD NOISE REDUCTION ALGORITHM DIAGNOSIS." Latin American Applied Research - An international journal 48, no. 3 (October 9, 2019): 229–33. http://dx.doi.org/10.52292/j.laar.2018.233.

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In order to extract the fault features of mechanical equipment submerged by strong background noise, a general subspace denoising algorithm based on Singular value decomposition (SVD) is used to process the signal, that is, μ-SVD denoising algorithm. This algorithm overcomes the disadvantage of traditional wavelet threshold denoising algorithm, which only deals with the wavelet coefficients point by point and ignores the whole structure of the wavelet coefficients. The traditional SVD denoising algorithm is a special case when Lagrange multiplier μ = 0 in μ-SVD denoising algorithm. μ-SVD denoising algorithm includes filter factor, which can suppress the contribution of singular value dominated by noise contribution to the signal after denoising. The parameter selection method of μ-SVD denoising algorithm is discussed, and the influence of denoising order and Lagrange multiplier on denoising effect is emphatically studied. The test results of gear fault simulation signal and gear fault vibration signal at the early stage show that μ-SVD denoising algorithm is better than traditional SVD denoising algorithm in denoising effect. It can extract gear fault features better under strong background noise.
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R. Tripathi, Mr Vijay. "Image Denoising." IOSR Journal of Engineering 1, no. 1 (November 2011): 84–87. http://dx.doi.org/10.9790/3021-0118487.

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Rissanen, J. "MDL denoising." IEEE Transactions on Information Theory 46, no. 7 (2000): 2537–43. http://dx.doi.org/10.1109/18.887861.

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Olhede, Sofia C. "Hyperanalytic Denoising." IEEE Transactions on Image Processing 16, no. 6 (June 2007): 1522–37. http://dx.doi.org/10.1109/tip.2007.896633.

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Giles, Kendall E., Michael W. Trosset, David J. Marchette, and Carey E. Priebe. "Iterative Denoising." Computational Statistics 23, no. 4 (October 12, 2007): 497–517. http://dx.doi.org/10.1007/s00180-007-0090-8.

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Lin, Fanqiang, Kecheng Chen, Xuben Wang, Hui Cao, Danlei Chen, and Fanzeng Chen. "Denoising stacked autoencoders for transient electromagnetic signal denoising." Nonlinear Processes in Geophysics 26, no. 1 (March 1, 2019): 13–23. http://dx.doi.org/10.5194/npg-26-13-2019.

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Abstract. The transient electromagnetic method (TEM) is extremely important in geophysics. However, the secondary field signal (SFS) in the TEM received by coil is easily disturbed by random noise, sensor noise and man-made noise, which results in the difficulty in detecting deep geological information. To reduce the noise interference and detect deep geological information, we apply autoencoders, which make up an unsupervised learning model in deep learning, on the basis of the analysis of the characteristics of the SFS to denoise the SFS. We introduce the SFSDSA (secondary field signal denoising stacked autoencoders) model based on deep neural networks of feature extraction and denoising. SFSDSA maps the signal points of the noise interference to the high-probability points with a clean signal as reference according to the deep characteristics of the signal, so as to realize the signal denoising and reduce noise interference. The method is validated by the measured data comparison, and the comparison results show that the noise reduction method can (i) effectively reduce the noise of the SFS in contrast with the Kalman, principal component analysis (PCA) and wavelet transform methods and (ii) strongly support the speculation of deeper underground features.
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Bertalmío, Marcelo, and Stacey Levine. "Denoising an Image by Denoising Its Curvature Image." SIAM Journal on Imaging Sciences 7, no. 1 (January 2014): 187–211. http://dx.doi.org/10.1137/120901246.

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He, Sun, and Wang. "Noise Reduction for MEMS Gyroscope Signal: A Novel Method Combining ACMP with Adaptive Multiscale SG Filter Based on AMA." Sensors 19, no. 20 (October 10, 2019): 4382. http://dx.doi.org/10.3390/s19204382.

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In this paper, a novel hybrid method combining adaptive chirp mode pursuit (ACMP) with an adaptive multiscale Savitzky–Golay filter (AMSGF) based on adaptive moving average (AMA) is proposed for offline denoising micro-electromechanical system (MEMS) gyroscope signal. The denoising scheme includes preliminary denoising and further denoising. At the preliminary denoising stage, the original gyroscope signal is decomposed into signal modes one by one using ACMP with modified stopping criterion based on mutual information. Useful information is extracted while most noise is discarded in the residue at this stage. Then, AMSGF is proposed to further denoise the signal modes. Sample variance based on AMA is used to adjust the window size of AMSGF adaptively. Practical MEMS gyroscope signal denoising results under different motion conditions show the superior performance of the proposed method over empirical mode decomposition (EMD)-based denoising, discrete wavelet threshold denoising, and variational mode decomposition (VMD)-based denoising. Moreover, AMSGF is proven to gain a better denoising effect than some other common smoothing methods.
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Kaur, Roopdeep, Gour Karmakar, and Muhammad Imran. "Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning." Applied Sciences 13, no. 20 (October 22, 2023): 11560. http://dx.doi.org/10.3390/app132011560.

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In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition. Various image-denoising approaches, including median, Gaussian, and bilateral filters, are available in the literature. Since convolutional neural networks (CNN) are able to directly learn complex patterns and features from data, they have become a popular choice for image-denoising tasks. As a result of their ability to learn and adapt to various denoising scenarios, CNNs are powerful tools for image denoising. Some deep learning techniques such as CNN incorporate denoising strategies directly into the CNN model layers. A primary limitation of these methods is their necessity to resize images to a consistent size. This resizing can result in a loss of vital image details, which might compromise CNN’s effectiveness. Because of this issue, we utilize a traditional denoising method as a preliminary step for noise reduction before applying CNN. To our knowledge, a comparative performance study of CNN using traditional and embedded denoising against a baseline approach (without denoising) is yet to be performed. To analyze the impact of denoising on the CNN performance, in this paper, firstly, we filter the noise from the images using traditional means of denoising method before their use in the CNN model. Secondly, we embed a denoising layer in the CNN model. To validate the performance of image denoising, we performed extensive experiments for both traffic sign and object recognition datasets. To decide whether denoising will be adopted and to decide on the type of filter to be used, we also present an approach exploiting the peak-signal-to-noise-ratio (PSNRs) distribution of images. Both CNN accuracy and PSNRs distribution are used to evaluate the effectiveness of the denoising approaches. As expected, the results vary with the type of filter, impact, and dataset used in both traditional and embedded denoising approaches. However, traditional denoising shows better accuracy, while embedded denoising shows lower computational time for most of the cases. Overall, this comparative study gives insights into whether denoising will be adopted in various CNN-based image analyses, including autonomous driving, animal detection, and facial recognition.
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Dissertations / Theses on the topic "Denoising"

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Kan, Hasan E. "Bootstrap based signal denoising." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://bosun.nps.edu/uhtbin/hyperion.exe/02Sep%5FKan.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 2002.
Thesis Advisor(s): Monique P. Fargues, Ralph D. Hippenstiel. "September 2002." Includes bibliographical references (p. 89-90). Also available in print.
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NIBHANUPUDI, SWATHI. "SIGNAL DENOISING USING WAVELETS." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1070577417.

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Ehret, Thibaud. "Video denoising and applications." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASN018.

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Cette thèse est dédiée es débruitage vidéo. La première partie se concentre sur les méthodes de débruitage de vidéo à patches. Nous étudions en détail VBM3D, une méthode populaire de débruitage vidéo, pour comprendre les méchanismes qui ont fait son succès. Nous présentons aussi une implémentation temps-réel sur care graphique de cette méthode. Nous étudions ensuite l'impacte de la recherche de patches pour le débruitage vidéo et en particulier commen une recherche globale peut améliorer la qualité du débruitage. Enfin, nous proposons une nouvelle méthode causale et récursive appelée NL-Kalman qui produit ne rès bonne consistance temporelle.Dans la deuxième partie, nous étudions les méthodes d'apprentissage pour le débruitage. Nous présentons l'une des toutes premières architecture de réseau qui est compétitive avec l'état de l'art. Nous montrons aussi que les méthodes basées sur l'apprentissage profond offrent de nouvelles opportunités. En particulier, il devient possible de débruiter sans connaître le modèle du bruit. Grâce à la méthode proposée, même les vidéos traitées par une chaîne de traitement inconnue peuvent être débruitées. Nous étudions aussi le cas de données mosaïquées. En particulier, nous montrons que les réseaux de neurones sont largement supérieurs aux méthodes précédentes. Nous proposons aussi une nouvelle méthode d'apprentissage pour démosaïckage sans avoir besoin de vérité terrain.Dans une troisième partie nous présentons différentes application aux techniques utilisées pour le débruitage. Le premier problème étudié est la détection d'anomalie. Nous montrons que ce problème peut être ramené à détecter des anomalies dans du bruit. Nous regardons aussi la détection de falsification et en particulier la détection de copié-collé. Tout comme le débruitage à patches, ce problème peut être résolu à l'aide d'une recherche de patches similaires. Pour cela, nous étudions en détail PatchMatch et l'utilisons pour détecter des falsifications. Nous présentons aussi une méthode basée sur une association de patches parcimonieuse
This thesis studies the problem of video denoising. In the first part we focus on patch-based video denoising methods. We study in details VBM3D, a popular video denoising method, to understand the mechanisms that made its success. We also present a real-time implementation on GPU of this method. We then study the impact of patch search in video denoising and in particular how searching for similar patches in the entire video, a global patch search, improves the denoising quality. Finally, we propose a novel causal and recursive method called NL-Kalman that produces very good temporal consistency.In the second part, we look at the newer trend of deep learning for image and video denoising. We present one of the first neural network architecture, using temporal self-similarity, competitive with state-of-the-art patch-based video denoising methods. We also show that deep learning offers new opportunities. In particular, it allows for denoising without knowing the noise model. We propose a framework that allows denoising of videos that have been through an unknown processing pipeline. We then look at the case of mosaicked data. In particular, we show that deep learning is undeniably superior to previous approaches for demosaicking. We also propose a novel training process for demosaicking without ground-truth based on multiple raw acquisition. This allows training for real case applications. In the third part we present different applications taking advantage of mechanisms similar those studied for denoising. The first problem studied is anomaly detection. We show that this problem can be reduced to detecting anomalies in noise. We also look at forgery detection and in particular copy-paste forgeries. Just like for patch-based denoising, solving this problem requires searching for similar patches. For that, we do an in-depth study of PatchMatch and see how it can be used for detecting forgeries. We also present an efficient method based on sparse patch matching
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Kan, Hasan Ertam. "Bootstrap based signal denoising." Thesis, Monterey, California. Naval Postgraduate School, 2002. http://hdl.handle.net/10945/2883.

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Approved for public release, distribution is unlimited
"This work accomplishes signal denoising using the Bootstrap method when the additive noise is Gaussian. The noisy signal is separated into frequency bands using the Fourier or Wavelet transform. Each frequency band is tested for Gaussianity by evaluating the kurtosis. The Bootstrap method is used to increase the reliability of the kurtosis estimate. Noise effects are minimized using a hard or soft thresholding scheme on the frequency bands that were estimated to be Gaussian. The recovered signal is obtained by applying the appropriate inverse transform to the modified frequency bands. The denoising scheme is tested using three test signals. Results show that FFT-based denoising schemes perform better than WT-based denoising schemes on the stationary sinusoidal signals, whereas WT-based schemes outperform FFT-based schemes on chirp type signals. Results also show that hard thresholding never outperforms soft thresholding, at best its performance is similar to soft thresholding."--p.i.
First Lieutenant, Turkish Army
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Gaspar, John M. "Denoising amplicon-based metagenomic data." Thesis, University of New Hampshire, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3581214.

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Reducing the effects of sequencing errors and PCR artifacts has emerged as an essential component in amplicon-based metagenomic studies. Denoising algorithms have been written that can reduce error rates in mock community data, in which the true sequences are known, but they were designed to be used in studies of real communities. To evaluate the outcome of the denoising process, we developed methods that do not rely on a priori knowledge of the correct sequences, and we applied these methods to a real-world dataset. We found that the denoising algorithms had substantial negative side-effects on the sequence data. For example, in the most widely used denoising pipeline, AmpliconNoise, the algorithm that was designed to remove pyrosequencing errors changed the reads in a manner inconsistent with the known spectrum of these errors, until one of the parameters was increased substantially from its default value.

With these shortcomings in mind, we developed a novel denoising program, FlowClus. FlowClus uses a systematic approach to filter and denoise reads efficiently. When denoising real datasets, FlowClus provides feedback about the process that can be used as the basis to adjust the parameters of the algorithm to suit the particular dataset. FlowClus produced a lower error rate compared to other denoising algorithms when analyzing a mock community dataset, while retaining significantly more sequence information. Among its other attributes, FlowClus can analyze longer reads being generated from current protocols and irregular flow orders. It has processed a full plate (1.5 million reads) in less than four hours; using its more efficient (but less precise) trie analysis option, this time was further reduced, to less than seven minutes.

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Bayreuther, Moritz, Jamin Cristall, and Felix J. Herrmann. "Curvelet denoising of 4d seismic." European Association of Geoscientists and Engineers, 2004. http://hdl.handle.net/2429/453.

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With burgeoning world demand and a limited rate of discovery of new reserves, there is increasing impetus upon the industry to optimize recovery from already existing fields. 4D, or time-lapse, seismic imaging is an emerging technology that holds great promise to better monitor and optimise reservoir production. The basic idea behind 4D seismic is that when multiple 3D surveys are acquired at separate calendar times over a producing field, the reservoir geology will not change from survey to survey but the state of the reservoir fluids will change. Thus, taking the difference between two 3D surveys should remove the static geologic contribution to the data and isolate the timevarying fluid flow component. However, a major challenge in 4D seismic is that acquisition and processing differences between 3D surveys often overshadow the changes caused by fluid flow. This problem is compounded when 4D effects are sought to be derived from vintage 3D data sets that were not originally acquired with 4D in mind. The goal of this study is to remove the acquisition and imaging artefacts from a 4D seismic difference cube using Curvelet processing techniques.
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Offei, Felix. "Denoising Tandem Mass Spectrometry Data." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3218.

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Protein identification using tandem mass spectrometry (MS/MS) has proven to be an effective way to identify proteins in a biological sample. An observed spectrum is constructed from the data produced by the tandem mass spectrometer. A protein can be identified if the observed spectrum aligns with the theoretical spectrum. However, data generated by the tandem mass spectrometer are affected by errors thus making protein identification challenging in the field of proteomics. Some of these errors include wrong calibration of the instrument, instrument distortion and noise. In this thesis, we present a pre-processing method, which focuses on the removal of noisy data with the hope of aiding in better identification of proteins. We employ the method of binning to reduce the number of noise peaks in the data without sacrificing the alignment of the observed spectrum with the theoretical spectrum. In some cases, the alignment of the two spectra improved.
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Ghazel, Mohsen. "Adaptive Fractal and Wavelet Image Denoising." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/882.

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The need for image enhancement and restoration is encountered in many practical applications. For instance, distortion due to additive white Gaussian noise (AWGN) can be caused by poor quality image acquisition, images observed in a noisy environment or noise inherent in communication channels. In this thesis, image denoising is investigated. After reviewing standard image denoising methods as applied in the spatial, frequency and wavelet domains of the noisy image, the thesis embarks on the endeavor of developing and experimenting with new image denoising methods based on fractal and wavelet transforms. In particular, three new image denoising methods are proposed: context-based wavelet thresholding, predictive fractal image denoising and fractal-wavelet image denoising. The proposed context-based thresholding strategy adopts localized hard and soft thresholding operators which take in consideration the content of an immediate neighborhood of a wavelet coefficient before thresholding it. The two fractal-based predictive schemes are based on a simple yet effective algorithm for estimating the fractal code of the original noise-free image from the noisy one. From this predicted code, one can then reconstruct a fractally denoised estimate of the original image. This fractal-based denoising algorithm can be applied in the pixel and the wavelet domains of the noisy image using standard fractal and fractal-wavelet schemes, respectively. Furthermore, the cycle spinning idea was implemented in order to enhance the quality of the fractally denoised estimates. Experimental results show that the proposed image denoising methods are competitive, or sometimes even compare favorably with the existing image denoising techniques reviewed in the thesis. This work broadens the application scope of fractal transforms, which have been used mainly for image coding and compression purposes.
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Rafi, Nazari Mina. "Denoising and Demosaicking of Color Images." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/35802.

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Most digital cameras capture images through Color Filter Arrays (CFA), and reconstruct the full color image from the CFA image. Each CFA pixel only captures one primary color component at each pixel location; the other primary components will be estimated using information from neighboring pixels. During the demosaicking algorithm, the unknown color components will be estimated at each pixel location. Most of the demosaicking algorithms use the RGB Bayer CFA pattern with Red, Green and Blue filters. Some other CFAs contain four color filters. The additional filter is a panchromatic/white filter, and it usually receives the full light spectrum. In this research, we studied and compared different four channel CFAs with panchromatic/white filter, and compared them with three channel CFAs. An appropriate demosaicking algorithm has been developed for each CFA. The most well-known three-channel CFA is Bayer. The Fujifilm X-Trans pattern has been studied in this work as another three-channel CFA with a different structure. Three different four-channel CFAs have been discussed in this research: RGBW-Kodak, RGBW-Bayer and RGBW- $5 \times 5$. The structure and the number of filters for each color are different for these CFAs. Since the Least-Square Luma-Chroma Demultiplexing method is a state of the art demosaicking method for the Bayer CFA, we designed the Least-Square method for RGBW CFAs. The effect of noise on different CFA patterns will be discussed for four channel CFAs. The Kodak database has been used to evaluate our non-adaptive and adaptive demosaicking methods as well as the optimized algorithms with the least square method. The captured values of white (panchromatic/clear) filters in RGBW CFAs have been estimated using red, green and blue filter values. Sets of optimized coefficients have been proposed to estimate the white filter values accurately. The results have been validated using the actual white values of a hyperspectral image dataset. A new denoising-demosaicking method for RGBW-Bayer CFA has been presented in this research. The algorithm has been tested on the Kodak dataset using the estimated value of white filters and a hyperspectral image dataset using the actual value of white filters, and the results have been compared. The results in both cases have been compared with the previous works on RGB-Bayer CFA, and it shows that the proposed algorithm using RGBW-Bayer CFA is working better than RGB-Bayer CFA in presence of noise.
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Gärdenäs, Anders Derk. "Denoising and renoising of videofor compression." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-340425.

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Videos contain increasingly more data due to increased resolutions. Codecs are further developed and improved to reduce the amount of data in videos. One difficulty with video encoding is noise handling, it's expensive to store noise and the final result is not always aesthetically pleasing. In this thesis project an algorithm is developed and presented which improves the visual quality while reducing the bit-rate of the video, by improved management of noise. The aim of the algorithm is to store noise information in a specific noise parameterinstead of mixing the noise with the visual information. The algorithm was developed to be part of the modern codec JEM, a successor of the h.264 and h.265 codecs. The algorithm can be summarized in the following steps: the first step is to identify how much noise there is in the video, which is done with a temporal noise identification algorithm. The noise identification is done at the start of the encoding process. The second step is to remove noise from the video with a denoising algorithm, this is done during the encoding processes. The third and final step is reapplication of the noise, this is done using the noise parameters computed in step one. The third step isdone during the decoding phase. The result was evaluated in a subjective survey consisting of five people evaluating 27 different versions of three videos. The result of the subjective survey shows a consistently improved visual qualityresulting from the proposed technique, achieving an improved score from 3.35 to 3.6on average on a subjective 1-5 scale where 5 is the best score. Furthermore, the bit-rate was significantly reduced by denoising. Bit-rate reduction is particularly high in high-quality videos, where the average reduction of as much as 49% is achieved. Another finding of this thesis is that the same video quality can be achieved using 2.7% less data by using a denoising tool as part of the video encoder. In conclusion, it ispossible to improve video quality while reducing the bit-rate using the proposed method.
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Books on the topic "Denoising"

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Mourad, Talbi. ECG Denoising Based on Total Variation Denoising and Wavelets. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25267-9.

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Escalera, Sergio, Stephane Ayache, Jun Wan, Meysam Madadi, Umut Güçlü, and Xavier Baró, eds. Inpainting and Denoising Challenges. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25614-2.

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Bertalmío, Marcelo, ed. Denoising of Photographic Images and Video. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96029-6.

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

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Tiwari, R. K., and R. Rekapalli. Modern Singular Spectral-Based Denoising and Filtering Techniques for 2D and 3D Reflection Seismic Data. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-19304-1.

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Paul, Sabyasachi. Use of wavelet based iterative filtering to improve denoising of spectral information for in-vivo gamma spectrometry. Mumbai, India: Bhabha Atomic Research Centre, 2012.

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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. Saarbrücken: VDM, Verlag Dr. Müller, 2008.

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ECG Denoising Based on Total Variation Denoising and Wavelets. Springer International Publishing AG, 2023.

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Wan, Jun, Sergio Escalera, Xavier Baró, Stephane Ayache, Meysam Madadi, and Umut Güçlü. Inpainting and Denoising Challenges. Springer, 2019.

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Wan, Jun, Sergio Escalera, Stephane Ayache, Meysam Madadi, and Umut Güçlü. Inpainting and Denoising Challenges. Springer International Publishing AG, 2020.

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Book chapters on the topic "Denoising"

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Majumdar, Angshul. "Denoising." In Compressed Sensing for Engineers, 251–60. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis, [2019] | Series: Devices, circuits, and systems: CRC Press, 2018. http://dx.doi.org/10.1201/9781351261364-15.

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Estrada, Francisco J. "Denoising." In Computer Vision, 177–79. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_484.

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Estrada, Francisco J. "Denoising." In Computer Vision, 283–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63416-2_484.

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Buchholz, Tim-Oliver, Mangal Prakash, Deborah Schmidt, Alexander Krull, and Florian Jug. "DenoiSeg: Joint Denoising and Segmentation." In Computer Vision – ECCV 2020 Workshops, 324–37. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66415-2_21.

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Lisowska, Agnieszka. "Image Denoising." In Geometrical Multiresolution Adaptive Transforms, 67–82. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05011-9_6.

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Elad, Michael. "Image Denoising." In Sparse and Redundant Representations, 273–307. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7011-4_14.

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Hadjileontiadis, Leontios J. "Denoising Techniques." In Lung Sounds, 61–81. Cham: Springer International Publishing, 2009. http://dx.doi.org/10.1007/978-3-031-01630-1_3.

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Aravind, B. N., K. V. Suresh, Nataraj H. D. Urs, N. Yashwanth, and Usha Desai. "Image Denoising." In Human-Machine Interface Technology Advancements and Applications, 181–212. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003326830-9.

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Mourad, Talbi. "A Denoising Technique Based on SBWT and WATV: Application for ECG Denoising." In ECG Denoising Based on Total Variation Denoising and Wavelets, 19–38. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25267-9_2.

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Mourad, Talbi. "Wavelets and Wavelet Transforms." In ECG Denoising Based on Total Variation Denoising and Wavelets, 1–18. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25267-9_1.

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Conference papers on the topic "Denoising"

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Franklin, Lena. "AI Denoising to Accelerate Detector Simulation." In AI Denoising to Accelerate Detector Simulation. US DOE, 2020. http://dx.doi.org/10.2172/1656622.

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Banerjee, Sunanda, Brian Rodriguez, Lena Franklin, Harold De La Cruz, Tara Leininger, Scarlet Norberg, Kevin Pedro, Angel Rosado Trinidad, and Yiheng Ye. "Denoising Convolutional Networks to Accelerate Detector Simulation." In Denoising Convolutional Networks to Accelerate Detector Simulation. US DOE, 2022. http://dx.doi.org/10.2172/1835859.

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Gondara, Lovedeep. "Medical Image Denoising Using Convolutional Denoising Autoencoders." In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE, 2016. http://dx.doi.org/10.1109/icdmw.2016.0041.

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Xiang, Qian, and Xuliang Pang. "Improved Denoising Auto-Encoders for Image Denoising." In 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2018. http://dx.doi.org/10.1109/cisp-bmei.2018.8633143.

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Welz, Joseph P., Matthew P. Iannacci, and David M. Jenkins. "Cavitation Detection Using Wavelet Denoising." In ASME 2004 Heat Transfer/Fluids Engineering Summer Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/ht-fed2004-56804.

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Abstract:
Cavitation in turbomachinery provides a source of damage to the hydrodynamic surfaces. Detection of cavitation at the earliest possible time after inception is desirable from a damage prevention standpoint. In order to detect cavitation in real time, acoustic sensing of the cavitation events has long been an accepted practice. A problem with this measurement technique is the potential contamination from electrical and acoustic background noise sources. This work employs an algorithm based on wavelet denoising. The wavelet denoising algorithm depends on a measurement of the acoustic background noise in the absence of cavitation. Cavitation measurements of a stationary object are evaluated with and without the application of the denoising process. The results of this comparison indicate that the wavelet denoising procedure allows an increased number of cavitation events to be detected at a given static pressure, and cavitation is detected at higher pressures than previous techniques.
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Gabarda, Salvador, Gabriel Cristobal, Lorenzo Galleani, and Leon Cohen. "Cloud denoising." In Second International Symposium on Fluctuations and Noise, edited by Derek Abbott, Sergey M. Bezrukov, Andras Der, and Angel Sanchez. SPIE, 2004. http://dx.doi.org/10.1117/12.547635.

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Li, Weijun, Jian Zou, Na Meng, Yuhong Fang, and Zheng Huang. "Evaluation of different denoising algorithms for OCT image denoising." In Optics in Health Care and Biomedical Optics X, edited by Qingming Luo, Xingde Li, Ying Gu, and Dan Zhu. SPIE, 2020. http://dx.doi.org/10.1117/12.2575240.

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Gao, Yan, Feng Gao, and Junyu Dong. "Hyperspectral Image Denoising Based on Multi-Stream Denoising Network." In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9553548.

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Wen, Jian, Jianfei Shao, Jianlong Shao, and Hongfei Pu. "Overview of traditional denoising and deep learning-based denoising." In 6th International Conference on Mechatronics and Intelligent Robotics, edited by Srikanta Patnaik and Tao Shen. SPIE, 2022. http://dx.doi.org/10.1117/12.2644503.

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Lv, Xiaowen, Zhenyu Xiong, Yourong Chen, and Ke Wang. "Improved stacked-denoising auto encoder for tide image denoising." In 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), edited by Yuriy S. Shmaliy, Yougang Sun, Habib Zaidi, Hongying Meng, Hoshang Kolivand, Jianping Luo, and Mamoun Alazab. SPIE, 2023. http://dx.doi.org/10.1117/12.3009252.

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Reports on the topic "Denoising"

1

Yufang, Bao. Nonlinear Image Denoising Methodologies. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada460128.

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Campbell, D., and R. Lanier. Wavelet Denoising of Mobile Radiation Data. Office of Scientific and Technical Information (OSTI), October 2007. http://dx.doi.org/10.2172/923100.

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Campbell, D. Wavelet Denoising of Mobile Radiation Data. Office of Scientific and Technical Information (OSTI), October 2008. http://dx.doi.org/10.2172/945527.

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Krim, H., J. C. Pesquet, and I. C. Schick. Parsimony and Wavelet Methods for Denoising. Fort Belvoir, VA: Defense Technical Information Center, April 1998. http://dx.doi.org/10.21236/ada459557.

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Lewis, Phillip, Antonio Gonzales, and Patrick Hammond. Evaluating Scalograms for Seismic Event Denoising. Office of Scientific and Technical Information (OSTI), September 2021. http://dx.doi.org/10.2172/1821851.

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Bruce, Andrew G., David L. Donoho, Hong-Ye Gao, and R. D. Martin. Denoising and Robust Non-Linear Wavelet Analysis,. Fort Belvoir, VA: Defense Technical Information Center, April 1994. http://dx.doi.org/10.21236/ada291668.

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Draelos, Timothy, and Dylan Fox. Microseismic Event Denoising: Removal of Borehole Waves. Office of Scientific and Technical Information (OSTI), December 2020. http://dx.doi.org/10.2172/1747001.

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Ahmed, Osama A. New Denoising Scheme for Magnetic Resonance Spectroscopy Signals. Fort Belvoir, VA: Defense Technical Information Center, October 2001. http://dx.doi.org/10.21236/ada410138.

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D'Elia, Marta, and De lo Reyes, Juan Carlos, Miniguano, Andres. Bilevel parameter optimization for nonlocal image denoising models. Office of Scientific and Technical Information (OSTI), November 2019. http://dx.doi.org/10.2172/1592945.

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Thompson, J. An Empirical Evaluation of Denoising Techniques for Streaming Data. Office of Scientific and Technical Information (OSTI), August 2014. http://dx.doi.org/10.2172/1165751.

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