Gotowe bibliografie tematyczne / Regularization by Denoising

Spis treści

  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Części książek
  4. Streszczenia konferencji

Gotowa bibliografia na temat „Regularization by Denoising”

Autor: Grafiati
Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "Regularization by Denoising"

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Lin, Huangxing, Yihong Zhuang, Xinghao Ding, et al. "Self-Supervised Image Denoising Using Implicit Deep Denoiser Prior." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 1586–94. http://dx.doi.org/10.1609/aaai.v37i2.25245.

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We devise a new regularization for denoising with self-supervised learning. The regularization uses a deep image prior learned by the network, rather than a traditional predefined prior. Specifically, we treat the output of the network as a ``prior'' that we again denoise after ``re-noising.'' The network is updated to minimize the discrepancy between the twice-denoised image and its prior. We demonstrate that this regularization enables the network to learn to denoise even if it has not seen any clean images. The effectiveness of our method is based on the fact that CNNs naturally tend to cap
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Prasath, V. "A well-posed multiscale regularization scheme for digital image denoising." International Journal of Applied Mathematics and Computer Science 21, no. 4 (2011): 769–77. http://dx.doi.org/10.2478/v10006-011-0061-7.

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A well-posed multiscale regularization scheme for digital image denoisingWe propose an edge adaptive digital image denoising and restoration scheme based on space dependent regularization. Traditional gradient based schemes use an edge map computed from gradients alone to drive the regularization. This may lead to the oversmoothing of the input image, and noise along edges can be amplified. To avoid these drawbacks, we make use of a multiscale descriptor given by a contextual edge detector obtained from local variances. Using a smooth transition from the computed edges, the proposed scheme rem
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Tan, 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 (2022): 012030. http://dx.doi.org/10.1088/1742-6596/2253/1/012030.

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Abstract Image restoration is a widely studied problem in the field of image processing. Although the existing image restoration methods based on denoising regularization have shown relatively well performance, image restoration methods for different features of unknown images have not been proposed. Since images have different features, it seems necessary to adopt different priori regular terms for different features. In this paper, we propose a multiscale image regularization denoising framework that can simultaneously perform two or more denoising prior regularization terms to better obtain
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Li, 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.

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Denoising is a fundamental task in hyperspectral image (HSI) processing that can improve the performance of classification, unmixing, and other subsequent applications. In an HSI, there is a large amount of local and global redundancy in its spatial domain that can be used to preserve the details and texture. In addition, the correlation of the spectral domain is another valuable property that can be utilized to obtain good results. Therefore, in this paper, we proposed a novel HSI denoising scheme that exploits composite spatial-spectral information using a nonlocal technique (NLT). First, a
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Li, Shu, Xi Yang, Haonan Liu, Yuwei Cai, and Zhenming Peng. "Seismic Data Denoising Based on Sparse and Low-Rank Regularization." Energies 13, no. 2 (2020): 372. http://dx.doi.org/10.3390/en13020372.

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Seismic denoising is a core task of seismic data processing. The quality of a denoising result directly affects data analysis, inversion, imaging and other applications. For the past ten years, there have mainly been two classes of methods for seismic denoising. One is based on the sparsity of seismic data. This kind of method can make use of the sparsity of seismic data in local area. The other is based on nonlocal self-similarity, and it can utilize the spatial information of seismic data. Sparsity and nonlocal self-similarity are important prior information. However, there is no seismic den
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Baloch, Gulsher, Huseyin Ozkaramanli, and Runyi Yu. "Residual Correlation Regularization Based Image Denoising." IEEE Signal Processing Letters 25, no. 2 (2018): 298–302. http://dx.doi.org/10.1109/lsp.2017.2789018.

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Chen, 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.

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Image denoising algorithm based on gradient dependent energy functional often compromised the image features like textures or certain details. This paper proposes an iterative regularization model based on Dual Norms for image denoising. By using iterative regularization model, the oscillating patterns of texture and detail are added back to fit and compute the original Dual Norms model, and the iterative behavior avoids overfull smoothing while denoising the features of textures and details to a certain extent. In addition, the iterative procedure is proposed in this paper, and the proposed a
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Guo, 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.

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Image denoising methods are often based on the minimization of an appropriately defined energy function. Many gradient dependent energy functions, such as Potts model and total variation denoising, regard image as piecewise constant function. In these methods, some important information such as edge sharpness and location is well preserved, but some detailed image feature like texture is often compromised in the process of denoising. For this reason, an image denoising method based on local adaptive regularization is proposed in this paper, which can adaptively adjust denoising degree of noisy
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Liu, 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.

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To avoid the staircase artifacts, an adaptive image denoising model is proposed by the weighted combination of Tikhonov regularization and total variation regularization. In our model, Tikhonov regularization and total variation regularization can be adaptively selected based on the gradient information of the image. When the pixels belong to the smooth regions, Tikhonov regularization is adopted, which can eliminate the staircase artifacts. When the pixels locate at the edges, total variation regularization is selected, which can preserve the edges. We employ the split Bregman method to solve
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Shen, Lixin, Bruce W. Suter, and Erin E. Tripp. "Algorithmic versatility of SPF-regularization methods." Analysis and Applications 19, no. 01 (2020): 43–69. http://dx.doi.org/10.1142/s0219530520400060.

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Sparsity promoting functions (SPFs) are commonly used in optimization problems to find solutions which are sparse in some basis. For example, the [Formula: see text]-regularized wavelet model and the Rudin–Osher–Fatemi total variation (ROF-TV) model are some of the most well-known models for signal and image denoising, respectively. However, recent work demonstrates that convexity is not always desirable in SPFs. In this paper, we replace convex SPFs with their induced nonconvex SPFs and develop algorithms for the resulting model by exploring the intrinsic structures of the nonconvex SPFs. The
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Rozprawy doktorskie na temat "Regularization by Denoising"

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Jalalzai, Khalid. "Regularization of inverse problems in image processing." Phd thesis, Ecole Polytechnique X, 2012. http://pastel.archives-ouvertes.fr/pastel-00787790.

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Les problèmes inverses consistent à retrouver une donnée qui a été transformée ou perturbée. Ils nécessitent une régularisation puisque mal posés. En traitement d'images, la variation totale en tant qu'outil de régularisation a l'avantage de préserver les discontinuités tout en créant des zones lisses, résultats établis dans cette thèse dans un cadre continu et pour des énergies générales. En outre, nous proposons et étudions une variante de la variation totale. Nous établissons une formulation duale qui nous permet de démontrer que cette variante coïncide avec la variation totale sur des ense
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Laruelo, Fernandez Andrea. "Integration of magnetic resonance spectroscopic imaging into the radiotherapy treatment planning." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30126/document.

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L'objectif de cette thèse est de proposer de nouveaux algorithmes pour surmonter les limitations actuelles et de relever les défis ouverts dans le traitement de l'imagerie spectroscopique par résonance magnétique (ISRM). L'ISRM est une modalité non invasive capable de fournir la distribution spatiale des composés biochimiques (métabolites) utilisés comme biomarqueurs de la maladie. Les informations fournies par l'ISRM peuvent être utilisées pour le diagnostic, le traitement et le suivi de plusieurs maladies telles que le cancer ou des troubles neurologiques. Cette modalité se montre utile en r
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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.

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The main contribution of this thesis is the concept of Fenchel duality with a focus on its application in the field of machine learning problems and image restoration tasks. We formulate a general optimization problem for modeling support vector machine tasks and assign a Fenchel dual problem to it, prove weak and strong duality statements as well as necessary and sufficient optimality conditions for that primal-dual pair. In addition, several special instances of the general optimization problem are derived for different choices of loss functions for both the regression and the classifificati
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Castellanos, 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.

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Actuellement, le principal obstacle à la mise en œuvre de la FWI élastique en trois dimensions sur des cas d'étude réalistes réside dans le coût de calcul associé aux taches de modélisation sismique. Pour surmonter cette difficulté, je propose deux contributions. Tout d'abord, je propose de calculer le gradient de la fonctionnelle avec la méthode de l'état adjoint à partir d'une forme symétrisée des équations de l'élastodynamique formulées sous forme d'un système du premier ordre en vitesse-contrainte. Cette formulation auto-adjointe des équations de l'élastodynamique permet de calculer les ch
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Nair, Pravin. "Provably Convergent Algorithms for Denoiser-Driven Image Regularization." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5887.

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Some fundamental reconstruction tasks in image processing can be posed as an inverse problem where we are required to invert a given forward model. For example, in deblurring and superresolution, the ground-truth image needs to be estimated from blurred and low-resolution images, whereas in CT and MR imaging, a high-resolution image must be reconstructed from a few linear measurements. Such inverse problems are invariably ill-posed—they exhibit non-unique solutions and the process of direct inversion is unstable. Some form of image model (or prior) on the ground truth is required to regu
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Gavaskar, Ruturaj G. "On Plug-and-Play Regularization using Linear Denoisers." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5973.

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The problem of inverting a given measurement model comes up in several computational imaging applications. For example, in CT and MRI, we are required to reconstruct a high-resolution image from incomplete noisy measurements, whereas in superresolution and deblurring, we try to infer the ground-truth from low-resolution or blurred images. Traditionally, this is done by minimizing $f + \phi$, where $f$ is a data-fidelity (or loss) function that is determined by the acquisition process, and $\phi$ is a regularization (or penalty) function that is based on a subjective prior on the target image.
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Michenková, Marie. "Regularizační metody založené na metodách nejmenších čtverců." Master's thesis, 2013. http://www.nusl.cz/ntk/nusl-330700.

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Title: Regularization Techniques Based on the Least Squares Method Author: Marie Michenková Department: Department of Numerical Mathematics Supervisor: RNDr. Iveta Hnětynková, Ph.D. Abstract: In this thesis we consider a linear inverse problem Ax ≈ b, where A is a linear operator with smoothing property and b represents an observation vector polluted by unknown noise. It was shown in [Hnětynková, Plešinger, Strakoš, 2009] that high-frequency noise reveals during the Golub-Kahan iterative bidiagonalization in the left bidiagonalization vectors. We propose a method that identifies the iteration
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Heinrich, 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.

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The main contribution of this thesis is the concept of Fenchel duality with a focus on its application in the field of machine learning problems and image restoration tasks. We formulate a general optimization problem for modeling support vector machine tasks and assign a Fenchel dual problem to it, prove weak and strong duality statements as well as necessary and sufficient optimality conditions for that primal-dual pair. In addition, several special instances of the general optimization problem are derived for different choices of loss functions for both the regression and the classifificati
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Części książek na temat "Regularization by Denoising"

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Lanza, Alessandro, Serena Morigi, and Fiorella Sgallari. "Convex Image Denoising via Non-Convex Regularization." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18461-6_53.

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Shi, Hui, Yann Traonmilin, and Jean-François Aujol. "Compressive Learning of Deep Regularization for Denoising." In Lecture Notes in Computer Science. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-31975-4_13.

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Lucchese, Mirko, Iuri Frosio, and N. Alberto Borghese. "Optimal Choice of Regularization Parameter in Image Denoising." In Image Analysis and Processing – ICIAP 2011. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24085-0_55.

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Calderon, Felix, and Carlos A. Júnez–Ferreira. "Regularization with Adaptive Neighborhood Condition for Image Denoising." In Advances in Soft Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25330-0_35.

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Peng, Yong, Shen Wang, and Bao-Liang Lu. "Marginalized Denoising Autoencoder via Graph Regularization for Domain Adaptation." In Neural Information Processing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42042-9_20.

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Ghoniem, Mahmoud, Youssef Chahir, and Abderrahim Elmoataz. "Video Denoising and Simplification Via Discrete Regularization on Graphs." In Advanced Concepts for Intelligent Vision Systems. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88458-3_34.

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Zhang, 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. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3023-9_47.

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Kim, 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. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02498-6_43.

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Li, 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. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70665-4_82.

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Lucchese, Mirko, and N. Alberto Borghese. "Denoising of Digital Radiographic Images with Automatic Regularization Based on Total Variation." In Image Analysis and Processing – ICIAP 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04146-4_76.

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Streszczenia konferencji na temat "Regularization by Denoising"

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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.

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Hu, 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.

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Bruni, 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.

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Hongyi 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.

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Clinchant, 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). Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/p16-2005.

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Carrera, 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.

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Ghoniem, 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.

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Xie, Qi, Qian Zhao, Deyu Meng, et al. "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.

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Rey, 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.

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Charest, 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.

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