Дисертації з теми "Denoising Image"
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Zhang, Jiachao. "Image denoising for real image sensors." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1437954286.
Повний текст джерелаGhazel, Mohsen. "Adaptive Fractal and Wavelet Image Denoising." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/882.
Повний текст джерелаLi, Zhi. "Variational image segmentation, inpainting and denoising." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/292.
Повний текст джерелаDanda, Swetha. "Generalized diffusion model for image denoising." Morgantown, W. Va. : [West Virginia University Libraries], 2007. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5481.
Повний текст джерелаTitle from document title page. Document formatted into pages; contains viii, 62 p. : ill. Includes abstract. Includes bibliographical references (p. 59-62).
Deng, Hao. "Mathematical approaches to digital color image denoising." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31708.
Повний текст джерелаCommittee Chair: Haomin Zhou; Committee Member: Luca Dieci; Committee Member: Ronghua Pan; Committee Member: Sung Ha Kang; Committee Member: Yang Wang. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Hussain, Israr. "Non-gaussianity based image deblurring and denoising." Thesis, University of Manchester, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.489022.
Повний текст джерелаSarjanoja, S. (Sampsa). "BM3D image denoising using heterogeneous computing platforms." Master's thesis, University of Oulu, 2015. http://urn.fi/URN:NBN:fi:oulu-201504141380.
Повний текст джерелаKohinanpoisto on yksi keskeisimmistä digitaaliseen kuvankäsittelyyn liittyvistä ongelmista, joka useimmiten pyritään ratkaisemaan jo signaalinkäsittelyvuon varhaisessa vaiheessa. Kohinaa ilmestyy kuviin monella eri tavalla ja sen esiintyminen on väistämätöntä. Useat kuvankäsittelyalgoritmit toimivat paremmin, jos niiden syöte on valmiiksi mahdollisimman virheetöntä käsiteltäväksi. Jotta kuvankäsittelyviiveet pysyisivät pieninä eri laskenta-alustoilla, on tärkeää että myös kohinanpoisto suoritetaan nopeasti. Viihdeteollisuuden kehityksen myötä näytönohjaimien laskentateho on moninkertaistunut. Nykyisin näytönohjainpiirit koostuvat useista sadoista tai jopa tuhansista laskentayksiköistä. Näiden laskentayksiköiden käyttäminen yleiskäyttöiseen laskentaan on mahdollista OpenCL- ja CUDA-ohjelmointirajapinnoilla. Rinnakkaislaskenta usealla laskentayksiköllä mahdollistaa suuria suorituskyvyn parannuksia käyttökohteissa, joissa käsiteltävä tieto on toisistaan riippumatonta tai löyhästi riippuvaista. Näytönohjainpiirien käyttö yleisessä laskennassa on yleistymässä myös mobiililaitteissa. Lisäksi valokuvaaminen on nykypäivänä suosituinta juuri mobiililaitteilla. Tämä diplomityö pyrkii selvittämään viimeisimmän kohinanpoistoon käytettävän tekniikan, lohkonsovitus ja kolmiulotteinen suodatus (block-matching and three-dimensional filtering, BM3D), laskennan toteuttamista heterogeenisissä laskentaympäristöissä. Työssä arvioidaan esiteltyjen toteutusten suorituskykyä tekemällä vertailuja jo olemassa oleviin toteutuksiin. Esitellyt toteutukset saavuttavat merkittäviä hyötyjä rinnakkaislaskennan käyttämisestä. Samalla vertailuissa havainnollistetaan yleisiä ongelmakohtia näytönohjainlaskennan hyödyntämisessä monimutkaisten kuvankäsittelyalgoritmien laskentaan
Houdard, Antoine. "Some advances in patch-based image denoising." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT005/document.
Повний текст джерелаThis thesis studies non-local methods for image processing, and their application to various tasks such as denoising. Natural images contain redundant structures, and this property can be used for restoration purposes. A common way to consider this self-similarity is to separate the image into "patches". These patches can then be grouped, compared and filtered together.In the first chapter, "global denoising" is reframed in the classical formalism of diagonal estimation and its asymptotic behaviour is studied in the oracle case. Precise conditions on both the image and the global filter are introduced to ensure and quantify convergence.The second chapter is dedicated to the study of Gaussian priors for patch-based image denoising. Such priors are widely used for image restoration. We propose some ideas to answer the following questions: Why are Gaussian priors so widely used? What information do they encode about the image?The third chapter proposes a probabilistic high-dimensional mixture model on the noisy patches. This model adopts a sparse modeling which assumes that the data lie on group-specific subspaces of low dimensionalities. This yields a denoising algorithm that demonstrates state-of-the-art performance.The last chapter explores different way of aggregating the patches together. A framework that expresses the patch aggregation in the form of a least squares problem is proposed
Karam, Christina Maria. "Acceleration of Non-Linear Image Filters, and Multi-Frame Image Denoising." University of Dayton / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1575976497271633.
Повний текст джерелаTuncer, Guney. "A Java Toolbox For Wavelet Based Image Denoising." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12608037/index.pdf.
Повний текст джерелаMichael, Simon. "A Comparison of Data Transformations in Image Denoising." Thesis, Uppsala universitet, Statistiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-375715.
Повний текст джерелаAparnnaa. "Image Denoising and Noise Estimation by Wavelet Transformation." Kent State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=kent1555929391906805.
Повний текст джерелаLind, Johan. "Evaluating CNN-based models for unsupervised image denoising." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176092.
Повний текст джерелаLiu, Xiaoyang. "Advanced numerical methods for image denoising and segmentation." Thesis, University of Greenwich, 2013. http://gala.gre.ac.uk/11954/.
Повний текст джерелаLiao, Zhiwu. "Image denoising using wavelet domain hidden Markov models." HKBU Institutional Repository, 2005. http://repository.hkbu.edu.hk/etd_ra/616.
Повний текст джерелаDe, Santis Simone. "Quantum Median Filter for Total Variation denoising." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.
Знайти повний текст джерелаZhang, Chen. "Blind Full Reference Quality Assessment of Poisson Image Denoising." University of Dayton / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1398875743.
Повний текст джерелаMcGraw, Tim E. "Denoising, segmentation and visualization of diffusion weighted MRI." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0011618.
Повний текст джерелаMaitree, Rapeepan, Gloria J. Guzman Perez-Carrillo, Joshua S. Shimony, H. Michael Gach, Anupama Chundury, Michael Roach, H. Harold Li, and Deshan Yang. "Adaptive anatomical preservation optimal denoising for radiation therapy daily MRI." SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 2017. http://hdl.handle.net/10150/626083.
Повний текст джерелаMiller, Sarah Victoria. "Mulit-Resolution Aitchison Geometry Image Denoising for Low-Light Photography." University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1596444315236623.
Повний текст джерелаLee, Kai-wah. "Mesh denoising and feature extraction from point cloud data." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B42664330.
Повний текст джерелаLee, Kai-wah, and 李啟華. "Mesh denoising and feature extraction from point cloud data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42664330.
Повний текст джерелаQuan, Jin. "Image Denoising of Gaussian and Poisson Noise Based on Wavelet Thresholding." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1380556846.
Повний текст джерелаJin, Xiaodan. "Poisson Approximation to Image Sensor Noise." University of Dayton / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1292306911.
Повний текст джерелаKim, Il-Ryeol. "Wavelet domain partition-based signal processing with applications to image denoising and compression." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 2.98 Mb., 119 p, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3221054.
Повний текст джерелаZhang, Chen. "Poisson Noise Parameter Estimation and Color Image Denoising for Real Camera Hardware." University of Dayton / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1575968356242716.
Повний текст джерелаKim, Yunho. "Variational methods theory and its applications to image deblurring and denoising problems /." Diss., Restricted to subscribing institutions, 2009. http://proquest.umi.com/pqdweb?did=1872146111&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.
Повний текст джерелаCheng, 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.
Повний текст джерелаBalster, 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.
Повний текст джерелаTitle 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).
Wang, Tianyang. "A Novel Image Retrieval Strategy Based on VPD and Depth with Pre-Processing." OpenSIUC, 2015. https://opensiuc.lib.siu.edu/dissertations/1054.
Повний текст джерелаQin, Jing. "Prior Information Guided Image Processing and Compressive Sensing." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1365020074.
Повний текст джерелаGeorge, Ben. "Statistical and Evolutionary Models for the Construction of Facial Composites and Image Denoising." Thesis, University of Kent, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.523637.
Повний текст джерелаLiu, Yang. "Image Denoising: Invertible and General Denoising Frameworks." Phd thesis, 2022. http://hdl.handle.net/1885/270008.
Повний текст джерелаHua, Gang. "Noncoherent image denoising." Thesis, 2005. http://hdl.handle.net/1911/17859.
Повний текст джерелаBao, Yufang. "Nonlinear image denoising methodologies." 2002. http://www.lib.ncsu.edu/theses/available/etd-05172002-131134/unrestricted/etd.pdf.
Повний текст джерелаCho, Dongwook. "Image denoising using wavelet transforms." Thesis, 2004. http://spectrum.library.concordia.ca/8141/1/MQ94737.pdf.
Повний текст джерелаParida, Satyabrata. "Denoising Of Satellite Images." Thesis, 2014. http://ethesis.nitrkl.ac.in/6612/1/Satyabrata_Parida_PROJECT_THESIS.pdf.
Повний текст джерелаMu-Yen, Chen, and 陳木炎. "Radar Image Denoising with Wavelet Packets." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/02378868527279744430.
Повний текст джерела國立海洋大學
航海技術學系
83
In this thesis, the frame of radar PPI image is treated as a whole for denoising via wavelet packets. To obtain a clean scan for radar observation, the object boundary in PPI image is considered more important than its texture content. Therefore, the complexity of denoising is reduced. Iterated noise estimation (INE) and recursively coefficients thresholding and reconstructing (RCTR) are two major steps is our denoising approach. It is shown that RCTR has better performance than the traditional inverse wavelet transform when the same coefficients thresholding is applied. Moreover, the performance is insensitive to the estimation error of noise variance by INE.
LIN, SIN-HONG, and 林信宏. "A wavelet-based image denoising method." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/9qf5dk.
Повний текст джерела國立臺北科技大學
自動化科技研究所
107
The efficient representation of edges is key to improving the image denoising performance. This motivates us to capture the edges and calculate them with a wavelet transform. A novel image denoising method is proposed by exploiting the image edges information and the multidirectional shrinkage. The image edges preservation effect is achieved by applying the main direction in the wavelet transform. Since the image is perform wavelet transform in different directions, for each pixel we obtain many different estimates, one of which is optimal. A noisy image is decomposed into subbands of LL, LH, HL, and HH in wavelet domain. LL subband contains the low frequency coefficients along with less noise, which can be easily eliminated using TV-based method. More edges and other detailed information like textures are contained in the other three subbands, and we propose a shrinkage method based on the local variance to extract them from high frequency noise. And apply adaptive threshold shrinkage to denoising. The final denoised output is obtained by a weighted averaging of all individual estimates. Experimental results show that our method, compared with other wavelet-based denoising algorithms, can effectively remove noise and preserve detail information such as edges and textures.
Lee, Ssu-Rui, and 李思叡. "Image Denoising by Convolutional Neural Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/he56yv.
Повний текст джерела國立清華大學
資訊系統與應用研究所
107
Removing noise from the images to improve image quality is the main challenge in image processing. Especially as the ubiquitous spread of computers, smartphones, the Internet, and social networks, image denoising becomes more and more important. In this work, we extend upon the results of Ulyanov et al.~\cite{Ulyanov_2018_CVPR} and introduce a competitive image denoising method based on the structure characteristic of convolutional neural networks (CNNs). Different from most CNN-based methods which need a large-scale dataset for training, our method only looks at one degraded image and removes noise on itself. This method is not only an application of image denoising but also a point of view for visualizing the property and effect of each element in convolutional neural networks.
Singh, Himanshu. "A Survey of Image Denoising Algorithms." Thesis, 2013. http://ethesis.nitrkl.ac.in/5454/1/109cs0191thesis.pdf.
Повний текст джерелаBhattacharya, Ranita. "Study of Color Image Denoising Filters." Thesis, 2016. http://ethesis.nitrkl.ac.in/9113/1/2016_MT_RBhattacharya.pdf.
Повний текст джерелаSantra, Ayan Kanti. "Denoising Images Under Multiplicative Noise." Thesis, 2013. http://ethesis.nitrkl.ac.in/4790/1/211EE1328.pdf.
Повний текст джерелаZhang, Wen. "General Adaptive Monte Carlo Bayesian Image Denoising." Thesis, 2010. http://hdl.handle.net/10012/4920.
Повний текст джерелаLee, Yu-lun, and 李育倫. "Diffusion Weighted Image Denoising by Wavelet Transform." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/45251434655663101996.
Повний текст джерела國立雲林科技大學
工業工程與管理研究所碩士班
99
Diffusion tensor imaging (DTI) is one of very common medical imaging, which composed by a series Diffusion weighted imaging. It is a non-invasive technique and it can provide the direction of nerve fiber in our brain. However, some noises, which generated by DTI rapid imaging, patients state and other factors, reduce the image quality of DWI. Noises make tractography produce incorrect result. In this study, we add noises in the simulation images, and use wavelet transform and anisotropic filter to denoising. We compare the performance of two filters, and choice a better filter used in practical images. The result of simulation data show that, wavelet transform significantly lower than anisotropic filter in average error. After denoising, the amplitude of measure index FA value average error shrink from 0.00832 to 0.001964, and MD value average error form 0.008096 to 0.002108, the average of measure index can be close to simulation image. There have four patients with metastatic tumor and four normal subjects in practical data. The regions of interest (ROI) include region of tumor, region of edema, region of edema symmetrical and region of white matter in normal subjects. We compute the measure index FA and MD, draw scatter plots, and then we compare the results. It show that, in different areas test, FA and MD value significantly different after denoising, in different subjects test, FA value significantly different after denoising, and MD value do not significantly different after denoising. The average deviation of FA value is 0.079775 and MD value is 0.038185. The result can be one of reference for medical imaging quantitative basis.
Chu, Chia-Min, and 朱家敏. "Adaptive Anisotropic Diffusion Equation for Image Denoising." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/98930738395777679872.
Повний текст джерела國立中興大學
應用數學系所
96
In this thesis, we present an adaptive correlation method for the image denoising and edge preservation. Several nonlinear diffusion filters have been widely implemented; however, it is still very difficult to preserve the image edge and clean the noise simultaneously. We propose a new anisotropic diffusion filter and consider an adaptive method that correlate a nonlinear diffusion filter with a linear low-pass filter for the smoothing. In numerical experiments, we compare our proposed methods with some popular nonlinear diffusion models and the result show the promise of the adaptive procedure removes the noisy and preserves the edge effectively.
Singh, Rajat, and Devendra Singh Meena. "Study of image denoising using curvelet transform." Thesis, 2013. http://ethesis.nitrkl.ac.in/5164/1/109CS0633.pdf.
Повний текст джерелаPatel, Kiran. "Hardware Architecture for Image Denoising Using DWT." Thesis, 2016. http://ethesis.nitrkl.ac.in/9299/1/2016_MT_KPatel.pdf.
Повний текст джерелаPanigrahi, Susant Kumar. "Image Denoising by Edge Preserved Curvelet Thresholding." Thesis, 2019. http://ethesis.nitrkl.ac.in/10040/1/2019_PhD_PSusantkumar_512EE102_Image.pdf.
Повний текст джерелаZhao, Hanqing. "Numerical Algorithms for Discrete Models of Image Denoising." Phd thesis, 2010. http://hdl.handle.net/10048/1165.
Повний текст джерелаMathematics