Relevant bibliographies by topics / Gradient Smoothing

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Gradient Smoothing'

Author: Grafiati
Published: 30 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Gradient Smoothing"

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Matsuoka, Ryo, та Masahiro Okuda. "Beyond Staircasing Effect: Robust Image Smoothing via ℓ0 Gradient Minimization and Novel Gradient Constraints". Signals 4, № 4 (2023): 669–86. http://dx.doi.org/10.3390/signals4040037.

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In this paper, we propose robust image-smoothing methods based on ℓ0 gradient minimization with novel gradient constraints to effectively suppress pseudo-edges. Simultaneously minimizing the ℓ0 gradient, i.e., the number of nonzero gradients in an image, and the ℓ2 data fidelity results in a smooth image. However, this optimization often leads to undesirable artifacts, such as pseudo-edges, known as the “staircasing effect”, and halos, which become more visible in image enhancement tasks, like detail enhancement and tone mapping. To address these issues, we introduce two types of gradient cons
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Zhou, Zhengyong, and Qi Yang. "An Active Set Smoothing Method for Solving Unconstrained Minimax Problems." Mathematical Problems in Engineering 2020 (June 24, 2020): 1–25. http://dx.doi.org/10.1155/2020/9108150.

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In this paper, an active set smoothing function based on the plus function is constructed for the maximum function. The active set strategy used in the smoothing function reduces the number of gradients and Hessians evaluations of the component functions in the optimization. Combing the active set smoothing function, a simple adjustment rule for the smoothing parameters, and an unconstrained minimization method, an active set smoothing method is proposed for solving unconstrained minimax problems. The active set smoothing function is continuously differentiable, and its gradient is locally Lip
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Fang, Shuai, Zhenji Yao, and Jing Zhang. "Scale and Gradient Aware Image Smoothing." IEEE Access 7 (2019): 166268–81. http://dx.doi.org/10.1109/access.2019.2953550.

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Wang, Dongdong, Jiarui Wang, and Junchao Wu. "Superconvergent gradient smoothing meshfree collocation method." Computer Methods in Applied Mechanics and Engineering 340 (October 2018): 728–66. http://dx.doi.org/10.1016/j.cma.2018.06.021.

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Zhang, Cong, Limin Sun, and Ya Xiao. "A Generalized Projetion Gradient Algorithm for Mathematical Programs with Complementary Constraints." Journal of Physics: Conference Series 2289, no. 1 (2022): 012019. http://dx.doi.org/10.1088/1742-6596/2289/1/012019.

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Abstract Against the shortcomings that many existing algorithms for solving the standard smoothing nonlinear programming would fail if they were used directly to solve the mathematical programs with complementary constraints( MPCC). By using a complementarity function and the idea of smoothing approximation method, the MPCC problem was transformed into a smoothing nonlinear programming. Combined with the supermemory gradient idea, a generalized projection gradient algorithm is proposed and its global convergence is obtained.
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Xu, Li, Cewu Lu, Yi Xu, and Jiaya Jia. "Image smoothing via L 0 gradient minimization." ACM Transactions on Graphics 30, no. 6 (2011): 1–12. http://dx.doi.org/10.1145/2070781.2024208.

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Burke, James V., Tim Hoheisel, and Christian Kanzow. "Gradient Consistency for Integral-convolution Smoothing Functions." Set-Valued and Variational Analysis 21, no. 2 (2013): 359–76. http://dx.doi.org/10.1007/s11228-013-0235-6.

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Yao, Jianyao, Weimin Wu, Kun Zhang, et al. "Development of Three-Dimensional GSM-CFD Solver for Compressible Flows." International Journal of Computational Methods 14, no. 04 (2017): 1750037. http://dx.doi.org/10.1142/s0219876217500372.

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A three-dimensional (3D) Computational Fluid Dynamics (CFD) solver based on the gradient smoothing method (GSM) is developed for compressible flows based on previous research. The piecewise constant smoothing function with one-point integration scheme is implemented for gradient approximation of field variables and convective fluxes. The matrix-based method for gradient approximations is also developed to improve the numerical efficiency. Numerical examples of gradient approximations of several given functions have shown that the proposed GSM is more accurate and robust to mesh distortion. A t
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Chuang, Ming, Szymon Rusinkiewicz, and Misha Kazhdan. "Gradient-Domain Processing of Meshes." Journal of Computer Graphics Techniques 5, no. 4 (2016): 44–55. https://doi.org/10.5281/zenodo.7953865.

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  This paper describes an implementation of gradient-domain processing for editing the geometry of triangle meshes in 3D. We show applications to mesh smoothing and sharpening and describe anisotropic extensions that enable edge-aware processing.
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Xu, Jingyan, and Frederic Noo. "Efficient gradient computation for optimization of hyperparameters." Physics in Medicine & Biology 67, no. 3 (2022): 03NT01. http://dx.doi.org/10.1088/1361-6560/ac4442.

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Abstract We are interested in learning the hyperparameters in a convex objective function in a supervised setting. The complex relationship between the input data to the convex problem and the desirable hyperparameters can be modeled by a neural network; the hyperparameters and the data then drive the convex minimization problem, whose solution is then compared to training labels. In our previous work (Xu and Noo 2021 Phys. Med. Biol. 66 19NT01), we evaluated a prototype of this learning strategy in an optimization-based sinogram smoothing plus FBP reconstruction framework. A question arising
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Dissertations / Theses on the topic "Gradient Smoothing"

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Lee, Chang-Kye. "Gradient smoothing in finite elasticity : near-incompressibility." Thesis, Cardiff University, 2016. http://orca.cf.ac.uk/94491/.

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This thesis presents the extension of the gradient smoothing technique for finite element approximation (so-called Smoothed Finite Element Method (S-FEM)) and its bubble-enhanced version for non-linear problems involving large deformations in nearly-incompressible and incompressible hyperelastic materials. Finite Element Method (FEM) presents numerous challenges for soft matter applications, such as incompressibility, complex geometries and mesh distortion from large deformation. S-FEM was introduced to overcome the challenges mentioned of FEM. The smoothed strains and the smoothed deformation
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Mao, Zirui. "A Novel Lagrangian Gradient Smoothing Method for Fluids and Flowing Solids." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1553252214052311.

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Pierucci, Federico. "Optimisation non-lisse pour l'apprentissage statistique avec régularisation matricielle structurée." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM024/document.

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La phase d’apprentissage des méthodes d’apprentissage statistique automatique correspondent à la résolution d’un problème d’optimisation mathématique dont la fonction objectif se décompose en deux parties: a) le risque empirique, construit à partir d’une fonction de perte, dont la forme est déterminée par la métrique de performance et les hypothèses sur le bruit; b) la pénalité de régularisation, construite a partir d’une norme ou fonction jauge, dont la structure est déterminée par l’information à priori disponible sur le problème a résoudre.Les fonctions de perte usuelles,
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Bhowmick, Sauradeep. "Advanced Smoothed Finite Element Modeling for Fracture Mechanics Analyses." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623240613376967.

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Mayrink, Victor Teixeira de Melo. "Avaliação do algoritmo Gradient Boosting em aplicações de previsão de carga elétrica a curto prazo." Universidade Federal de Juiz de Fora (UFJF), 2016. https://repositorio.ufjf.br/jspui/handle/ufjf/3563.

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Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-03-07T14:25:21Z No. of bitstreams: 1 victorteixeirademelomayrink.pdf: 2587774 bytes, checksum: 1319cc37a15480796050b618b4d7e5f7 (MD5)<br>Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-03-07T15:06:57Z (GMT) No. of bitstreams: 1 victorteixeirademelomayrink.pdf: 2587774 bytes, checksum: 1319cc37a15480796050b618b4d7e5f7 (MD5)<br>Made available in DSpace on 2017-03-07T15:06:57Z (GMT). No. of bitstreams: 1 victorteixeirademelomayrink.pdf: 2587774 bytes, checksum: 1319cc37a15480796050b618b4d7e5f7
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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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Huang, Chih-Ping, and 黃志平. "Piecewise Linear Function Solution Space and Modified-Gradient Smoothing Domain Method." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/51737947931004919031.

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Cheng, Ching Wen, and 鄭景文. "Simplification Of Centroid Gradient Smoothing Domain Method Using Finite Element Basis." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/52320644772318750123.

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Jhong, Jhih-Syong, and 鍾智雄. "A Study of Gradient Smoothing Methods for Boundary Value Problems on Triangular Meshes." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/93662964677132066520.

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Hsieh, Hsun, and 謝. 洵. "Automatic tumor segmentation of breast ultra-sound images using a distance-regularized level-set evolution method with initial contour obtained by guided image filter, L0 gradient minimization smoothing pre-processing, and morphological features." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/t6z6cs.

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碩士<br>國立清華大學<br>電機工程學系所<br>105<br>Due to the speckle noise and low contrast in breast ultrasound images, it is hard to locate the contour of the tumor by using a single method. In this thesis, a new method for finding an initial contour is proposed, which can improve the result of DRLSE on the segmentation of BUS images. The new method focuses on improving the algorithm proposed by Tsai-Wen Niu, which is a way to search an initial contour based on the local minimum in the images. When the BUS images contain calcification, it is possible to fail in searching of initial contour through such algo
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Books on the topic "Gradient Smoothing"

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Geological Survey (U.S.), ed. Combining edge-gradient information to improve adaptive discontinuity-preserving smoothing of multispectral images. U.S. Geological Survey, 1994.

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Geological Survey (U.S.), ed. Combining edge-gradient information to improve adaptive discontinuity-preserving smoothing of multispectral images. U.S. Geological Survey, 1994.

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Geological Survey (U.S.), ed. Combining edge-gradient information to improve adaptive discontinuity-preserving smoothing of multispectral images. U.S. Geological Survey, 1994.

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Geological Survey (U.S.), ed. Combining edge-gradient information to improve adaptive discontinuity-preserving smoothing of multispectral images. U.S. Geological Survey, 1994.

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Liu, Gui-Rong, and Zirui Mao. Gradient Smoothing Methods Programminghb : Gradient Smoothing Methods with Programming: Applications to Fluids and Landslides. World Scientific Publishing Co Pte Ltd, 2024.

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Combining edge-gradient information to improve adaptive discontinuity-preserving smoothing of multispectral images. U.S. Geological Survey, 1994.

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Combining edge-gradient information to improve adaptive discontinuity-preserving smoothing of multispectral images. U.S. Geological Survey, 1994.

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Book chapters on the topic "Gradient Smoothing"

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Bui, Tinh Quoc. "A Smoothing Gradient-Enhanced Damage Model." In Computational and Experimental Simulations in Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27053-7_9.

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Welk, Martin. "Diffusion, Pre-smoothing and Gradient Descent." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75549-2_7.

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Zhang, He, François Petitjean, and Wray Buntine. "Hierarchical Gradient Smoothing for Probability Estimation Trees." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47426-3_18.

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Howlett, John, and Alan Zundel. "Size Function Smoothing Using an Element Area Gradient." In Proceedings of the 18th International Meshing Roundtable. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04319-2_1.

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Khajwal, Basim, C. H. Luke Ong, and Dominik Wagner. "Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing." In Programming Languages and Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30044-8_18.

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AbstractWe study the foundations of variational inference, which frames posterior inference as an optimisation problem, for probabilistic programming. The dominant approach for optimisation in practice is stochastic gradient descent. In particular, a variant using the so-called reparameterisation gradient estimator exhibits fast convergence in a traditional statistics setting. Unfortunately, discontinuities, which are readily expressible in programming languages, can compromise the correctness of this approach. We consider a simple (higher-order, probabilistic) programming language with condit
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Cox, Ingemar J., Sunita Hingorani, Bruce M. Maggs, and Satish B. Rao. "Stereo Without Disparity Gradient Smoothing: a Bayesian Sensor Fusion Solution." In BMVC92. Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-3201-1_35.

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Ahmad, Zohaib, Kaizhe Nie, Junfei Qiao, and Cuili Yang. "Batch Gradient Training Method with Smoothing $$l_0$$ Regularization for Echo State Networks." In Machine Learning and Intelligent Communications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32388-2_42.

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Chen, Li, Hongzhi Zhang, Dongwei Ren, David Zhang, and Wangmeng Zuo. "Fast Augmented Lagrangian Method for Image Smoothing with Hyper-Laplacian Gradient Prior." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45643-9_2.

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Mikriukov, Georgii, Gesina Schwalbe, Christian Hellert, and Korinna Bade. "Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44067-0_26.

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AbstractAnalysis of how semantic concepts are represented within Convolutional Neural Networks (CNNs) is a widely used approach in Explainable Artificial Intelligence (XAI) for interpreting CNNs. A motivation is the need for transparency in safety-critical AI-based systems, as mandated in various domains like automated driving. However, to use the concept representations for safety-relevant purposes, like inspection or error retrieval, these must be of high quality and, in particular, stable. This paper focuses on two stability goals when working with concept representations in computer vision
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Ul Rahman, Jamshaid, Akhtar Ali, Masood Ur Rehman, and Rafaqat Kazmi. "A Unit Softmax with Laplacian Smoothing Stochastic Gradient Descent for Deep Convolutional Neural Networks." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5232-8_14.

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Conference papers on the topic "Gradient Smoothing"

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Lee, Donghyun, Jinhoon Wang, Beomsu Cho, and Junghyun Oh. "Enhancing Neural Implicit Representation-Based SLAM Performance through Depth Image Smoothing Utilizing Gradient-Aware Depth." In 2024 24th International Conference on Control, Automation and Systems (ICCAS). IEEE, 2024. https://doi.org/10.23919/iccas63016.2024.10773027.

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Prokhorov, Kirill, and Alexandr A. Kalinin. "Improving Acne Image Grading with Label Distribution Smoothing." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635668.

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Akai, Yuji, Toshihiro Shibata, Ryo Matsuoka, and Masahiro Okuda. "L0 Smoothing Based on Gradient Constraints." In 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. http://dx.doi.org/10.1109/icip.2018.8451436.

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Pinilla, Samuel, Jorge Bacca, Jhon Angarita, and Henry Arguello. "Phase Retrieval via Smoothing Projected Gradient Method." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8461445.

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Jiao, Jian, Hong Lu, Zijian Wang, Wenqiang Zhang, and Lizhe Qi. "L0 Gradient Smoothing and Bimodal Histogram Analysis." In MMAsia '19: ACM Multimedia Asia. ACM, 2019. http://dx.doi.org/10.1145/3338533.3366554.

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Feng Huang, Hu Cheng, and S. Vijayakumar. "Gradient weighted smoothing for MRI intensity correction." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1617109.

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Gudkov, Vladimir, and Ilia Moiseev. "Image Smoothing Algorithm Based on Gradient Analysis." In 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). IEEE, 2020. http://dx.doi.org/10.1109/usbereit48449.2020.9117646.

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Subhan, Fazli, Salman Ahmed, and Khalid Ashraf. "Extended Gradient Predictor and Filter for smoothing RSSI." In 2014 16th International Conference on Advanced Communication Technology (ICACT). Global IT Research Institute (GIRI), 2014. http://dx.doi.org/10.1109/icact.2014.6779148.

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Heiden, Eric, Luigi Palmieri, Sven Koenig, Kai O. Arras, and Gaurav S. Sukhatme. "Gradient-Informed Path Smoothing for Wheeled Mobile Robots." In 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018. http://dx.doi.org/10.1109/icra.2018.8460818.

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Knyazev, Andrew, and Alexander Malyshev. "Conjugate gradient acceleration of non-linear smoothing filters." In 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2015. http://dx.doi.org/10.1109/globalsip.2015.7418194.

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