Добірка наукової літератури з теми "Regularization by Denoising"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Regularization by Denoising".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Regularization by Denoising"
Lin, Huangxing, Yihong Zhuang, Xinghao Ding, Delu Zeng, Yue Huang, Xiaotong Tu, and John Paisley. "Self-Supervised Image Denoising Using Implicit Deep Denoiser Prior." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (June 26, 2023): 1586–94. http://dx.doi.org/10.1609/aaai.v37i2.25245.
Повний текст джерелаPrasath, V. "A well-posed multiscale regularization scheme for digital image denoising." International Journal of Applied Mathematics and Computer Science 21, no. 4 (December 1, 2011): 769–77. http://dx.doi.org/10.2478/v10006-011-0061-7.
Повний текст джерелаTan, Yi, Jin Fan, Dong Sun, Qingwei Gao, and Yixiang Lu. "Multi-scale Image Denoising via a Regularization Method." Journal of Physics: Conference Series 2253, no. 1 (April 1, 2022): 012030. http://dx.doi.org/10.1088/1742-6596/2253/1/012030.
Повний текст джерелаLi, Ao, Deyun Chen, Kezheng Lin, and Guanglu Sun. "Hyperspectral Image Denoising with Composite Regularization Models." Journal of Sensors 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/6586032.
Повний текст джерелаLi, Shu, Xi Yang, Haonan Liu, Yuwei Cai, and Zhenming Peng. "Seismic Data Denoising Based on Sparse and Low-Rank Regularization." Energies 13, no. 2 (January 13, 2020): 372. http://dx.doi.org/10.3390/en13020372.
Повний текст джерелаBaloch, Gulsher, Huseyin Ozkaramanli, and Runyi Yu. "Residual Correlation Regularization Based Image Denoising." IEEE Signal Processing Letters 25, no. 2 (February 2018): 298–302. http://dx.doi.org/10.1109/lsp.2017.2789018.
Повний текст джерелаChen, Guan Nan, Dan Er Xu, Rong Chen, Zu Fang Huang, and Zhong Jian Teng. "Iterative Regularization Model for Image Denoising Based on Dual Norms." Applied Mechanics and Materials 182-183 (June 2012): 1245–49. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1245.
Повний текст джерелаGuo, Li, Weilong Chen, Yu Liao, Honghua Liao, and Jun Li. "An Edge-Preserved Image Denoising Algorithm Based on Local Adaptive Regularization." Journal of Sensors 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/2019569.
Повний текст джерелаLiu, Kui, Jieqing Tan, and Benyue Su. "An Adaptive Image Denoising Model Based on Tikhonov and TV Regularizations." Advances in Multimedia 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/934834.
Повний текст джерелаShen, Lixin, Bruce W. Suter, and Erin E. Tripp. "Algorithmic versatility of SPF-regularization methods." Analysis and Applications 19, no. 01 (July 3, 2020): 43–69. http://dx.doi.org/10.1142/s0219530520400060.
Повний текст джерелаДисертації з теми "Regularization by Denoising"
Jalalzai, Khalid. "Regularization of inverse problems in image processing." Phd thesis, Ecole Polytechnique X, 2012. http://pastel.archives-ouvertes.fr/pastel-00787790.
Повний текст джерелаLaruelo, Fernandez Andrea. "Integration of magnetic resonance spectroscopic imaging into the radiotherapy treatment planning." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30126/document.
Повний текст джерелаThe aim of this thesis is to propose new algorithms to overcome the current limitations and to address the open challenges in the processing of magnetic resonance spectroscopic imaging (MRSI) data. MRSI is a non-invasive modality able to provide the spatial distribution of relevant biochemical compounds (metabolites) commonly used as biomarkers of disease. Information provided by MRSI can be used as a valuable insight for the diagnosis, treatment and follow-up of several diseases such as cancer or neurological disorders. Obtaining accurate and reliable information from in vivo MRSI signals is a crucial requirement for the clinical utility of this technique. Despite the numerous publications on the topic, the interpretation of MRSI data is still a challenging problem due to different factors such as the low signal-to-noise ratio (SNR) of the signals, the overlap of spectral lines or the presence of nuisance components. This thesis addresses the problem of interpreting MRSI data and characterizing recurrence in tumor brain patients. These objectives are addressed through a methodological approach based on novel processing methods that incorporate prior knowledge on the MRSI data using a spatio-spectral regularization. As an application, the thesis addresses the integration of MRSI into the radiotherapy treatment workflow within the context of the European project SUMMER (Software for the Use of Multi-Modality images in External Radiotherapy) founded by the European Commission (FP7-PEOPLE-ITN framework)
Heinrich, André. "Fenchel duality-based algorithms for convex optimization problems with applications in machine learning and image restoration." Doctoral thesis, Universitätsbibliothek Chemnitz, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-108923.
Повний текст джерелаCastellanos, Lopez Clara. "Accélération et régularisation de la méthode d'inversion des formes d'ondes complètes en exploration sismique." Phd thesis, Université Nice Sophia Antipolis, 2014. http://tel.archives-ouvertes.fr/tel-01064412.
Повний текст джерелаNair, Pravin. "Provably Convergent Algorithms for Denoiser-Driven Image Regularization." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5887.
Повний текст джерелаGavaskar, Ruturaj G. "On Plug-and-Play Regularization using Linear Denoisers." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5973.
Повний текст джерелаMichenková, Marie. "Regularizační metody založené na metodách nejmenších čtverců." Master's thesis, 2013. http://www.nusl.cz/ntk/nusl-330700.
Повний текст джерелаHeinrich, André. "Fenchel duality-based algorithms for convex optimization problems with applications in machine learning and image restoration." Doctoral thesis, 2012. https://monarch.qucosa.de/id/qucosa%3A19869.
Повний текст джерелаЧастини книг з теми "Regularization by Denoising"
Lanza, Alessandro, Serena Morigi, and Fiorella Sgallari. "Convex Image Denoising via Non-Convex Regularization." In Lecture Notes in Computer Science, 666–77. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18461-6_53.
Повний текст джерелаShi, Hui, Yann Traonmilin, and Jean-François Aujol. "Compressive Learning of Deep Regularization for Denoising." In Lecture Notes in Computer Science, 162–74. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-31975-4_13.
Повний текст джерелаLucchese, Mirko, Iuri Frosio, and N. Alberto Borghese. "Optimal Choice of Regularization Parameter in Image Denoising." In Image Analysis and Processing – ICIAP 2011, 534–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24085-0_55.
Повний текст джерелаCalderon, Felix, and Carlos A. Júnez–Ferreira. "Regularization with Adaptive Neighborhood Condition for Image Denoising." In Advances in Soft Computing, 398–406. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25330-0_35.
Повний текст джерелаPeng, Yong, Shen Wang, and Bao-Liang Lu. "Marginalized Denoising Autoencoder via Graph Regularization for Domain Adaptation." In Neural Information Processing, 156–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42042-9_20.
Повний текст джерелаGhoniem, Mahmoud, Youssef Chahir, and Abderrahim Elmoataz. "Video Denoising and Simplification Via Discrete Regularization on Graphs." In Advanced Concepts for Intelligent Vision Systems, 380–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88458-3_34.
Повний текст джерелаZhang, J. W., J. Liu, Y. H. Zheng, and J. Wang. "Regularization Parameter Selection for Gaussian Mixture Model Based Image Denoising Method." In Advances in Computer Science and Ubiquitous Computing, 291–97. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3023-9_47.
Повний текст джерелаKim, Yunho, Paul M. Thompson, Arthur W. Toga, Luminita Vese, and Liang Zhan. "HARDI Denoising: Variational Regularization of the Spherical Apparent Diffusion Coefficient sADC." In Lecture Notes in Computer Science, 515–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02498-6_43.
Повний текст джерелаLi, Li, Xiaohong Shen, and Shanshan Gao. "Image Denoising Using Expected Patch Log Likelihood and Hyper-laplacian Regularization." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 761–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70665-4_82.
Повний текст джерелаLucchese, Mirko, and N. Alberto Borghese. "Denoising of Digital Radiographic Images with Automatic Regularization Based on Total Variation." In Image Analysis and Processing – ICIAP 2009, 711–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04146-4_76.
Повний текст джерелаТези доповідей конференцій з теми "Regularization by Denoising"
Bahia, B., and M. Sacchi. "Deblending via Regularization by Denoising." In 82nd EAGE Annual Conference & Exhibition. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202011992.
Повний текст джерелаHu, Yuyang, Jiaming Liu, Xiaojian Xu, and Ulugbek S. Kamilov. "Monotonically Convergent Regularization by Denoising." In 2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2022. http://dx.doi.org/10.1109/icip46576.2022.9897639.
Повний текст джерелаBruni, V., and D. Vitulano. "Signal and image denoising without regularization." In 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, 2013. http://dx.doi.org/10.1109/icip.2013.6738111.
Повний текст джерелаHongyi Liu and Zhihui Wei. "Structure-preserved NLTV regularization for image denoising." In 2011 International Conference on Image Analysis and Signal Processing (IASP). IEEE, 2011. http://dx.doi.org/10.1109/iasp.2011.6109033.
Повний текст джерелаClinchant, Stephane, Gabriela Csurka, and Boris Chidlovskii. "A Domain Adaptation Regularization for Denoising Autoencoders." In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/p16-2005.
Повний текст джерелаCarrera, Anthony, Adrian Basarab, and Roberto Lavarello. "Attenuation coefficient imaging using regularization by denoising." In 2022 IEEE International Ultrasonics Symposium (IUS). IEEE, 2022. http://dx.doi.org/10.1109/ius54386.2022.9957734.
Повний текст джерелаGhoniem, Mahmoud, Youssef Chahir, and Abderrahim Elmoataz. "Video denoising via discrete regularization on graphs." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761412.
Повний текст джерелаXie, Qi, Qian Zhao, Deyu Meng, Zongben Xu, Shuhang Gu, Wangmeng Zuo, and Lei Zhang. "Multispectral Images Denoising by Intrinsic Tensor Sparsity Regularization." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.187.
Повний текст джерелаRey, Samuel, and Antonio G. Marques. "Robust Graph-Filter Identification with Graph Denoising Regularization." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9414909.
Повний текст джерелаCharest, Michael, Michael Elad, and Peyman Milanfar. "A General Iterative Regularization Framework For Image Denoising." In 2006 40th Annual Conference on Information Sciences and Systems. IEEE, 2006. http://dx.doi.org/10.1109/ciss.2006.286510.
Повний текст джерела