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Artykuły w czasopismach na temat "Denoising Image"
Rubel, Andrii, Oleksii Rubel, Vladimir Lukin i Karen Egiazarian. "Decision-making on image denoising expedience". Electronic Imaging 2021, nr 10 (18.01.2021): 237–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.10.ipas-237.
Pełny tekst źródłaR. Tripathi, Mr Vijay. "Image Denoising". IOSR Journal of Engineering 1, nr 1 (listopad 2011): 84–87. http://dx.doi.org/10.9790/3021-0118487.
Pełny tekst źródłaXu, Shaoping, Xiaojun Chen, Yiling Tang, Shunliang Jiang, Xiaohui Cheng i Nan Xiao. "Learning from Multiple Instances: A Two-Stage Unsupervised Image Denoising Framework Based on Deep Image Prior". Applied Sciences 12, nr 21 (24.10.2022): 10767. http://dx.doi.org/10.3390/app122110767.
Pełny tekst źródłaHuang, Tingsheng, Chunyang Wang i Xuelian Liu. "Depth Image Denoising Algorithm Based on Fractional Calculus". Electronics 11, nr 12 (19.06.2022): 1910. http://dx.doi.org/10.3390/electronics11121910.
Pełny tekst źródłaBertalmío, Marcelo, i Stacey Levine. "Denoising an Image by Denoising Its Curvature Image". SIAM Journal on Imaging Sciences 7, nr 1 (styczeń 2014): 187–211. http://dx.doi.org/10.1137/120901246.
Pełny tekst źródłaKhan, Aamir, Weidong Jin, Amir Haider, MuhibUr Rahman i Desheng Wang. "Adversarial Gaussian Denoiser for Multiple-Level Image Denoising". Sensors 21, nr 9 (24.04.2021): 2998. http://dx.doi.org/10.3390/s21092998.
Pełny tekst źródłaGavini, Venkateswarlu, i Gurusamy Ramasamy Jothi Lakshmi. "CT Image Denoising Model Using Image Segmentation for Image Quality Enhancement for Liver Tumor Detection Using CNN". Traitement du Signal 39, nr 5 (30.11.2022): 1807–14. http://dx.doi.org/10.18280/ts.390540.
Pełny tekst źródłaZhang, Xiangning, Yan Yang i Lening Lin. "Edge-aware image denoising algorithm". Journal of Algorithms & Computational Technology 13 (30.10.2018): 174830181880477. http://dx.doi.org/10.1177/1748301818804774.
Pełny tekst źródłaManjón, José V., Neil A. Thacker, Juan J. Lull, Gracian Garcia-Martí, Luís Martí-Bonmatí i Montserrat Robles. "Multicomponent MR Image Denoising". International Journal of Biomedical Imaging 2009 (2009): 1–10. http://dx.doi.org/10.1155/2009/756897.
Pełny tekst źródłaBadgainya, Shruti, Prof Pankaj Sahu i Prof Vipul Awasthi. "Image Denoising by OWT for Gaussian Noise Corrupted Images". International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (31.08.2018): 2477–84. http://dx.doi.org/10.31142/ijtsrd18337.
Pełny tekst źródłaRozprawy doktorskie na temat "Denoising Image"
Zhang, Jiachao. "Image denoising for real image sensors". University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1437954286.
Pełny tekst źródłaGhazel, Mohsen. "Adaptive Fractal and Wavelet Image Denoising". Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/882.
Pełny tekst źródłaLi, Zhi. "Variational image segmentation, inpainting and denoising". HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/292.
Pełny tekst źródłaDanda, Swetha. "Generalized diffusion model for image denoising". Morgantown, W. Va. : [West Virginia University Libraries], 2007. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5481.
Pełny tekst źródłaTitle 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.
Pełny tekst źródłaCommittee 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.
Pełny tekst źródłaSarjanoja, S. (Sampsa). "BM3D image denoising using heterogeneous computing platforms". Master's thesis, University of Oulu, 2015. http://urn.fi/URN:NBN:fi:oulu-201504141380.
Pełny tekst źródłaKohinanpoisto 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.
Pełny tekst źródłaThis 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.
Pełny tekst źródłaTuncer, Guney. "A Java Toolbox For Wavelet Based Image Denoising". Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12608037/index.pdf.
Pełny tekst źródłaKsiążki na temat "Denoising Image"
Shukla, K. K. Efficient Algorithms for Discrete Wavelet Transform: With Applications to Denoising and Fuzzy Inference Systems. London: Springer London, 2013.
Znajdź pełny tekst źródłaBertalmío, Marcelo, red. Denoising of Photographic Images and Video. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96029-6.
Pełny tekst źródłaKok, Chi-Wah, i Wing-Shan Tam. Digital Image Denoising in MATLAB. Wiley & Sons, Incorporated, John, 2022.
Znajdź pełny tekst źródłaKok, Chi-Wah, i Wing-Shan Tam. Digital Image Denoising in MATLAB. Wiley & Sons, Incorporated, John, 2022.
Znajdź pełny tekst źródłaKok, Chi-Wah, i Wing-Shan Tam. Digital Image Denoising in MATLAB. Wiley & Sons, Limited, John, 2022.
Znajdź pełny tekst źródłaKok, Chi-Wah, i Wing-Shan Tam. Digital Image Denoising in MATLAB. Wiley & Sons, Incorporated, John, 2022.
Znajdź pełny tekst źródłaShukla, K. K., i Arvind K. Tiwari. Efficient Algorithms for Discrete Wavelet Transform: With Applications to Denoising and Fuzzy Inference Systems. Springer London, Limited, 2013.
Znajdź pełny tekst źródłaBertalmío, Marcelo. Denoising of Photographic Images and Video: Fundamentals, Open Challenges and New Trends. Springer, 2018.
Znajdź pełny tekst źródłaBertalmío, Marcelo. Denoising of Photographic Images and Video: Fundamentals, Open Challenges and New Trends. Springer, 2018.
Znajdź pełny tekst źródłaCzęści książek na temat "Denoising Image"
Lisowska, Agnieszka. "Image Denoising". W Geometrical Multiresolution Adaptive Transforms, 67–82. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05011-9_6.
Pełny tekst źródłaElad, Michael. "Image Denoising". W 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.
Pełny tekst źródłaAravind, B. N., K. V. Suresh, Nataraj H. D. Urs, N. Yashwanth i Usha Desai. "Image Denoising". W Human-Machine Interface Technology Advancements and Applications, 181–212. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003326830-9.
Pełny tekst źródłaGomo, Panganai. "PageRank Image Denoising". W Lecture Notes in Computer Science, 1–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13772-3_1.
Pełny tekst źródłaXiao, Yao, Kai Huang, Hely Lin i Ruogu Fang. "Medical Imaging Denoising". W Medical Image Synthesis, 99–119. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003243458-10.
Pełny tekst źródłaRadow, Georg, Michael Breuß, Laurent Hoeltgen i Thomas Fischer. "Optimised Anisotropic Poisson Denoising". W Image Analysis, 502–14. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59126-1_42.
Pełny tekst źródłaZhang, Jiangang, Xiang Pan i Tianxu Lv. "Unsupervised MRI Images Denoising via Decoupled Expression". W Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 769–77. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_77.
Pełny tekst źródłaLisowska, Agnieszka. "Multiwedgelets in Image Denoising". W Lecture Notes in Electrical Engineering, 3–11. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6738-6_1.
Pełny tekst źródłaKoziarski, Michał, i Bogusław Cyganek. "Deep Neural Image Denoising". W Computer Vision and Graphics, 163–73. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46418-3_15.
Pełny tekst źródłaKumbhar, Mursal Furqan. "Image Denoising Using Autoencoders". W Artificial Intelligence and Knowledge Processing, 137–44. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003328414-13.
Pełny tekst źródłaStreszczenia konferencji na temat "Denoising Image"
Yue, Huanjing, Xiaoyan Sun, Jingyu Yang i Feng Wu. "Image denoising using cloud images". W SPIE Optical Engineering + Applications, redaktor Andrew G. Tescher. SPIE, 2013. http://dx.doi.org/10.1117/12.2022506.
Pełny tekst źródłaEstrada, Francisco, David Fleet i Allan Jepson. "Stochastic Image Denoising". W British Machine Vision Conference 2009. British Machine Vision Association, 2009. http://dx.doi.org/10.5244/c.23.117.
Pełny tekst źródłaLiu, Yang, Saeed Anwar, Liang Zheng i Qi Tian. "GradNet Image Denoising". W 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2020. http://dx.doi.org/10.1109/cvprw50498.2020.00262.
Pełny tekst źródłaAravind, B. N., i K. V. Suresh. "Hybrid image denoising". W 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT). IEEE, 2017. http://dx.doi.org/10.1109/iceeccot.2017.8284524.
Pełny tekst źródłaKattakinda, Priyatham, i A. N. Rajagopalan. "Unpaired Image Denoising". W 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9190932.
Pełny tekst źródłaS. B, Anuja, i Ramesh Dhanaseelan F. "Denoising of Diabetic Retinopathy Images Using Adaptive Median Filter". W The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/gxpd6690/ngcesi23p15.
Pełny tekst źródłaНасонов, Андрей, Andrey Nasonov, Николай Мамаев, Nikolay Mamaev, Ольга Володина, Olga Volodina, Андрей Крылов i Andrey Krylov. "Automatic Choice of Denoising Parameter in Perona-Malik Model". W 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-2-144-147.
Pełny tekst źródłaGondara, Lovedeep. "Medical Image Denoising Using Convolutional Denoising Autoencoders". W 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE, 2016. http://dx.doi.org/10.1109/icdmw.2016.0041.
Pełny tekst źródłaXiang, Qian, i Xuliang Pang. "Improved Denoising Auto-Encoders for Image Denoising". W 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.
Pełny tekst źródłaJain, Arti, i Anand Singh Jalal. "An Effective Image Denoising Approach Based on Denoising with Image Interpolation". W 2023 IEEE World Conference on Applied Intelligence and Computing (AIC). IEEE, 2023. http://dx.doi.org/10.1109/aic57670.2023.10263909.
Pełny tekst źródłaRaporty organizacyjne na temat "Denoising Image"
Yufang, Bao. Nonlinear Image Denoising Methodologies. Fort Belvoir, VA: Defense Technical Information Center, maj 2002. http://dx.doi.org/10.21236/ada460128.
Pełny tekst źródłaD'Elia, Marta, i De lo Reyes, Juan Carlos, Miniguano, Andres. Bilevel parameter optimization for nonlocal image denoising models. Office of Scientific and Technical Information (OSTI), listopad 2019. http://dx.doi.org/10.2172/1592945.
Pełny tekst źródłaD'Elia, Marta, Juan Carlos De los Reyes i Andres Trujillo. Bilevel parameter optimization for learning nonlocal image denoising models. Office of Scientific and Technical Information (OSTI), kwiecień 2020. http://dx.doi.org/10.2172/1617438.
Pełny tekst źródłaPotts, Catherine Gabriel. Visual Data: Technical Diagrams. Denoising of Technical Diagram Images. Office of Scientific and Technical Information (OSTI), sierpień 2019. http://dx.doi.org/10.2172/1558025.
Pełny tekst źródłaNifong, Nathaniel. Learning General Features From Images and Audio With Stacked Denoising Autoencoders. Portland State University Library, styczeń 2000. http://dx.doi.org/10.15760/etd.1549.
Pełny tekst źródłaTadmor, Eitan, Suzanne Nezzar i Luminita Vese. Multiscale Hierarchical Decomposition of Images with Applications to Deblurring, Denoising and Segmentation. Fort Belvoir, VA: Defense Technical Information Center, listopad 2007. http://dx.doi.org/10.21236/ada489758.
Pełny tekst źródłaLevesque, Joseph. Neural network denoising of HED x-ray images, with an introduction to neural networks. Office of Scientific and Technical Information (OSTI), kwiecień 2023. http://dx.doi.org/10.2172/1970268.
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