Literatura académica sobre el tema "Denoising Image"
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Artículos de revistas sobre el tema "Denoising Image"
Rubel, Andrii, Oleksii Rubel, Vladimir Lukin y Karen Egiazarian. "Decision-making on image denoising expedience". Electronic Imaging 2021, n.º 10 (18 de enero de 2021): 237–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.10.ipas-237.
Texto completoR. Tripathi, Mr Vijay. "Image Denoising". IOSR Journal of Engineering 1, n.º 1 (noviembre de 2011): 84–87. http://dx.doi.org/10.9790/3021-0118487.
Texto completoXu, Shaoping, Xiaojun Chen, Yiling Tang, Shunliang Jiang, Xiaohui Cheng y Nan Xiao. "Learning from Multiple Instances: A Two-Stage Unsupervised Image Denoising Framework Based on Deep Image Prior". Applied Sciences 12, n.º 21 (24 de octubre de 2022): 10767. http://dx.doi.org/10.3390/app122110767.
Texto completoHuang, Tingsheng, Chunyang Wang y Xuelian Liu. "Depth Image Denoising Algorithm Based on Fractional Calculus". Electronics 11, n.º 12 (19 de junio de 2022): 1910. http://dx.doi.org/10.3390/electronics11121910.
Texto completoBertalmío, Marcelo y Stacey Levine. "Denoising an Image by Denoising Its Curvature Image". SIAM Journal on Imaging Sciences 7, n.º 1 (enero de 2014): 187–211. http://dx.doi.org/10.1137/120901246.
Texto completoKhan, Aamir, Weidong Jin, Amir Haider, MuhibUr Rahman y Desheng Wang. "Adversarial Gaussian Denoiser for Multiple-Level Image Denoising". Sensors 21, n.º 9 (24 de abril de 2021): 2998. http://dx.doi.org/10.3390/s21092998.
Texto completoGavini, Venkateswarlu y 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, n.º 5 (30 de noviembre de 2022): 1807–14. http://dx.doi.org/10.18280/ts.390540.
Texto completoZhang, Xiangning, Yan Yang y Lening Lin. "Edge-aware image denoising algorithm". Journal of Algorithms & Computational Technology 13 (30 de octubre de 2018): 174830181880477. http://dx.doi.org/10.1177/1748301818804774.
Texto completoManjón, José V., Neil A. Thacker, Juan J. Lull, Gracian Garcia-Martí, Luís Martí-Bonmatí y Montserrat Robles. "Multicomponent MR Image Denoising". International Journal of Biomedical Imaging 2009 (2009): 1–10. http://dx.doi.org/10.1155/2009/756897.
Texto completoBadgainya, Shruti, Prof Pankaj Sahu y 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 de agosto de 2018): 2477–84. http://dx.doi.org/10.31142/ijtsrd18337.
Texto completoTesis sobre el tema "Denoising Image"
Zhang, Jiachao. "Image denoising for real image sensors". University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1437954286.
Texto completoGhazel, Mohsen. "Adaptive Fractal and Wavelet Image Denoising". Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/882.
Texto completoLi, Zhi. "Variational image segmentation, inpainting and denoising". HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/292.
Texto completoDanda, Swetha. "Generalized diffusion model for image denoising". Morgantown, W. Va. : [West Virginia University Libraries], 2007. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5481.
Texto completoTitle 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.
Texto completoCommittee 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.
Texto completoSarjanoja, S. (Sampsa). "BM3D image denoising using heterogeneous computing platforms". Master's thesis, University of Oulu, 2015. http://urn.fi/URN:NBN:fi:oulu-201504141380.
Texto completoKohinanpoisto 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.
Texto completoThis 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.
Texto completoTuncer, Guney. "A Java Toolbox For Wavelet Based Image Denoising". Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12608037/index.pdf.
Texto completoLibros sobre el tema "Denoising Image"
Shukla, K. K. Efficient Algorithms for Discrete Wavelet Transform: With Applications to Denoising and Fuzzy Inference Systems. London: Springer London, 2013.
Buscar texto completoBertalmío, Marcelo, ed. Denoising of Photographic Images and Video. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96029-6.
Texto completoKok, Chi-Wah y Wing-Shan Tam. Digital Image Denoising in MATLAB. Wiley & Sons, Incorporated, John, 2022.
Buscar texto completoKok, Chi-Wah y Wing-Shan Tam. Digital Image Denoising in MATLAB. Wiley & Sons, Incorporated, John, 2022.
Buscar texto completoKok, Chi-Wah y Wing-Shan Tam. Digital Image Denoising in MATLAB. Wiley & Sons, Limited, John, 2022.
Buscar texto completoKok, Chi-Wah y Wing-Shan Tam. Digital Image Denoising in MATLAB. Wiley & Sons, Incorporated, John, 2022.
Buscar texto completoShukla, K. K. y Arvind K. Tiwari. Efficient Algorithms for Discrete Wavelet Transform: With Applications to Denoising and Fuzzy Inference Systems. Springer London, Limited, 2013.
Buscar texto completoBertalmío, Marcelo. Denoising of Photographic Images and Video: Fundamentals, Open Challenges and New Trends. Springer, 2018.
Buscar texto completoBertalmío, Marcelo. Denoising of Photographic Images and Video: Fundamentals, Open Challenges and New Trends. Springer, 2018.
Buscar texto completoCapítulos de libros sobre el tema "Denoising Image"
Lisowska, Agnieszka. "Image Denoising". En Geometrical Multiresolution Adaptive Transforms, 67–82. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05011-9_6.
Texto completoElad, Michael. "Image Denoising". En 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.
Texto completoAravind, B. N., K. V. Suresh, Nataraj H. D. Urs, N. Yashwanth y Usha Desai. "Image Denoising". En Human-Machine Interface Technology Advancements and Applications, 181–212. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003326830-9.
Texto completoGomo, Panganai. "PageRank Image Denoising". En 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.
Texto completoXiao, Yao, Kai Huang, Hely Lin y Ruogu Fang. "Medical Imaging Denoising". En Medical Image Synthesis, 99–119. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003243458-10.
Texto completoRadow, Georg, Michael Breuß, Laurent Hoeltgen y Thomas Fischer. "Optimised Anisotropic Poisson Denoising". En Image Analysis, 502–14. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59126-1_42.
Texto completoZhang, Jiangang, Xiang Pan y Tianxu Lv. "Unsupervised MRI Images Denoising via Decoupled Expression". En 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.
Texto completoLisowska, Agnieszka. "Multiwedgelets in Image Denoising". En Lecture Notes in Electrical Engineering, 3–11. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6738-6_1.
Texto completoKoziarski, Michał y Bogusław Cyganek. "Deep Neural Image Denoising". En Computer Vision and Graphics, 163–73. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46418-3_15.
Texto completoKumbhar, Mursal Furqan. "Image Denoising Using Autoencoders". En Artificial Intelligence and Knowledge Processing, 137–44. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003328414-13.
Texto completoActas de conferencias sobre el tema "Denoising Image"
Yue, Huanjing, Xiaoyan Sun, Jingyu Yang y Feng Wu. "Image denoising using cloud images". En SPIE Optical Engineering + Applications, editado por Andrew G. Tescher. SPIE, 2013. http://dx.doi.org/10.1117/12.2022506.
Texto completoEstrada, Francisco, David Fleet y Allan Jepson. "Stochastic Image Denoising". En British Machine Vision Conference 2009. British Machine Vision Association, 2009. http://dx.doi.org/10.5244/c.23.117.
Texto completoLiu, Yang, Saeed Anwar, Liang Zheng y Qi Tian. "GradNet Image Denoising". En 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2020. http://dx.doi.org/10.1109/cvprw50498.2020.00262.
Texto completoAravind, B. N. y K. V. Suresh. "Hybrid image denoising". En 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT). IEEE, 2017. http://dx.doi.org/10.1109/iceeccot.2017.8284524.
Texto completoKattakinda, Priyatham y A. N. Rajagopalan. "Unpaired Image Denoising". En 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9190932.
Texto completoS. B, Anuja y Ramesh Dhanaseelan F. "Denoising of Diabetic Retinopathy Images Using Adaptive Median Filter". En 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.
Texto completoНасонов, Андрей, Andrey Nasonov, Николай Мамаев, Nikolay Mamaev, Ольга Володина, Olga Volodina, Андрей Крылов y Andrey Krylov. "Automatic Choice of Denoising Parameter in Perona-Malik Model". En 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.
Texto completoGondara, Lovedeep. "Medical Image Denoising Using Convolutional Denoising Autoencoders". En 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE, 2016. http://dx.doi.org/10.1109/icdmw.2016.0041.
Texto completoXiang, Qian y Xuliang Pang. "Improved Denoising Auto-Encoders for Image Denoising". En 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.
Texto completoJain, Arti y Anand Singh Jalal. "An Effective Image Denoising Approach Based on Denoising with Image Interpolation". En 2023 IEEE World Conference on Applied Intelligence and Computing (AIC). IEEE, 2023. http://dx.doi.org/10.1109/aic57670.2023.10263909.
Texto completoInformes sobre el tema "Denoising Image"
Yufang, Bao. Nonlinear Image Denoising Methodologies. Fort Belvoir, VA: Defense Technical Information Center, mayo de 2002. http://dx.doi.org/10.21236/ada460128.
Texto completoD'Elia, Marta y De lo Reyes, Juan Carlos, Miniguano, Andres. Bilevel parameter optimization for nonlocal image denoising models. Office of Scientific and Technical Information (OSTI), noviembre de 2019. http://dx.doi.org/10.2172/1592945.
Texto completoD'Elia, Marta, Juan Carlos De los Reyes y Andres Trujillo. Bilevel parameter optimization for learning nonlocal image denoising models. Office of Scientific and Technical Information (OSTI), abril de 2020. http://dx.doi.org/10.2172/1617438.
Texto completoPotts, Catherine Gabriel. Visual Data: Technical Diagrams. Denoising of Technical Diagram Images. Office of Scientific and Technical Information (OSTI), agosto de 2019. http://dx.doi.org/10.2172/1558025.
Texto completoNifong, Nathaniel. Learning General Features From Images and Audio With Stacked Denoising Autoencoders. Portland State University Library, enero de 2000. http://dx.doi.org/10.15760/etd.1549.
Texto completoTadmor, Eitan, Suzanne Nezzar y Luminita Vese. Multiscale Hierarchical Decomposition of Images with Applications to Deblurring, Denoising and Segmentation. Fort Belvoir, VA: Defense Technical Information Center, noviembre de 2007. http://dx.doi.org/10.21236/ada489758.
Texto completoLevesque, Joseph. Neural network denoising of HED x-ray images, with an introduction to neural networks. Office of Scientific and Technical Information (OSTI), abril de 2023. http://dx.doi.org/10.2172/1970268.
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