Total Normal Curvature Regularization and its Minimization for Surface and Image Smoothing
- URL: http://arxiv.org/abs/2512.18968v2
- Date: Thu, 25 Dec 2025 14:02:39 GMT
- Title: Total Normal Curvature Regularization and its Minimization for Surface and Image Smoothing
- Authors: Tianle Lu, Ke Chen, Yuping Duan,
- Abstract summary: We introduce a novel formulation for curvature regularization by penalizing normal curvatures from multiple directions.<n>This total normal curvature regularization is capable of producing solutions with sharp edges and precise isotropic properties.<n>We reformulate it as the task of finding the steady-state solution of a time-dependent partial differential equation.
- Score: 9.60191212857289
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce a novel formulation for curvature regularization by penalizing normal curvatures from multiple directions. This total normal curvature regularization is capable of producing solutions with sharp edges and precise isotropic properties. To tackle the resulting high-order nonlinear optimization problem, we reformulate it as the task of finding the steady-state solution of a time-dependent partial differential equation (PDE) system. Time discretization is achieved through operator splitting, where each subproblem at the fractional steps either has a closed-form solution or can be efficiently solved using advanced algorithms. Our method circumvents the need for complex parameter tuning and demonstrates robustness to parameter choices. The efficiency and effectiveness of our approach have been rigorously validated in the context of surface and image smoothing problems.
Related papers
- Enhancing Distributional Robustness in Principal Component Analysis by Wasserstein Distances [7.695578200868269]
We consider the distributionally robust optimization (DRO) model of principal component analysis (PCA) to account for uncertainty in the underlying probability distribution.<n>The resulting formulation leads to a nonsmooth constrained min-max optimization problem, where the ambiguity set captures the distributional uncertainty by the type-$2$ Wasserstein distance.<n>This explicit characterization equivalently reformulates the original DRO model into a minimization problem on the Stiefel manifold with intricate nonsmooth terms.
arXiv Detail & Related papers (2025-03-04T11:00:08Z) - Meshless Shape Optimization using Neural Networks and Partial Differential Equations on Graphs [1.3812010983144802]
We present a fully meshless level set framework that leverages neural networks to parameterize the level set function and employs the graph Laplacian to approximate the underlying PDE.<n>Our approach enables precise computations of geometric quantities such as surface normals and curvature, and allows tackling optimization problems within the class of convex shapes.
arXiv Detail & Related papers (2025-02-20T18:42:27Z) - Adaptive Gradient Normalization and Independent Sampling for (Stochastic) Generalized-Smooth Optimization [23.962901840695462]
We show that existing algorithms are not fully adapted to generalized nonsmooth geometry.<n>Experiments show the fast convergence of sampling problems with our algorithm.
arXiv Detail & Related papers (2024-10-17T21:52:00Z) - Trust-Region Sequential Quadratic Programming for Stochastic Optimization with Random Models [57.52124921268249]
We propose a Trust Sequential Quadratic Programming method to find both first and second-order stationary points.
To converge to first-order stationary points, our method computes a gradient step in each iteration defined by minimizing a approximation of the objective subject.
To converge to second-order stationary points, our method additionally computes an eigen step to explore the negative curvature the reduced Hessian matrix.
arXiv Detail & Related papers (2024-09-24T04:39:47Z) - A simple uniformly optimal method without line search for convex optimization [9.280355951055865]
We show that line search is superfluous in attaining the optimal rate of convergence for solving a convex optimization problem whose parameters are not given a priori.
We present a novel accelerated gradient descent type algorithm called AC-FGM that can achieve an optimal $mathcalO (1/k2)$ rate of convergence for smooth convex optimization.
arXiv Detail & Related papers (2023-10-16T05:26:03Z) - Constrained Optimization via Exact Augmented Lagrangian and Randomized
Iterative Sketching [55.28394191394675]
We develop an adaptive inexact Newton method for equality-constrained nonlinear, nonIBS optimization problems.
We demonstrate the superior performance of our method on benchmark nonlinear problems, constrained logistic regression with data from LVM, and a PDE-constrained problem.
arXiv Detail & Related papers (2023-05-28T06:33:37Z) - A conditional gradient homotopy method with applications to Semidefinite Programming [1.3332839594069592]
homotopy-based conditional gradient method for solving convex optimization problems with a large number of simple conic constraints.<n>Our theoretical complexity is competitive when confronted to state-of-the-art SDP, with the decisive advantage of cheap projection-frees.
arXiv Detail & Related papers (2022-07-07T05:48:27Z) - An Operator-Splitting Method for the Gaussian Curvature Regularization
Model with Applications in Surface Smoothing and Imaging [6.860238280163609]
We propose an operator-splitting method for a general Gaussian curvature model.
The proposed method is not sensitive to the choice of parameters, its efficiency and performances being demonstrated.
arXiv Detail & Related papers (2021-08-04T08:59:41Z) - Implicit differentiation for fast hyperparameter selection in non-smooth
convex learning [87.60600646105696]
We study first-order methods when the inner optimization problem is convex but non-smooth.
We show that the forward-mode differentiation of proximal gradient descent and proximal coordinate descent yield sequences of Jacobians converging toward the exact Jacobian.
arXiv Detail & Related papers (2021-05-04T17:31:28Z) - Pushing the Envelope of Rotation Averaging for Visual SLAM [69.7375052440794]
We propose a novel optimization backbone for visual SLAM systems.
We leverage averaging to improve the accuracy, efficiency and robustness of conventional monocular SLAM systems.
Our approach can exhibit up to 10x faster with comparable accuracy against the state-art on public benchmarks.
arXiv Detail & Related papers (2020-11-02T18:02:26Z) - Conditional gradient methods for stochastically constrained convex
minimization [54.53786593679331]
We propose two novel conditional gradient-based methods for solving structured convex optimization problems.
The most important feature of our framework is that only a subset of the constraints is processed at each iteration.
Our algorithms rely on variance reduction and smoothing used in conjunction with conditional gradient steps, and are accompanied by rigorous convergence guarantees.
arXiv Detail & Related papers (2020-07-07T21:26:35Z) - Convergence of adaptive algorithms for weakly convex constrained
optimization [59.36386973876765]
We prove the $mathcaltilde O(t-1/4)$ rate of convergence for the norm of the gradient of Moreau envelope.
Our analysis works with mini-batch size of $1$, constant first and second order moment parameters, and possibly smooth optimization domains.
arXiv Detail & Related papers (2020-06-11T17:43:19Z) - Effective Dimension Adaptive Sketching Methods for Faster Regularized
Least-Squares Optimization [56.05635751529922]
We propose a new randomized algorithm for solving L2-regularized least-squares problems based on sketching.
We consider two of the most popular random embeddings, namely, Gaussian embeddings and the Subsampled Randomized Hadamard Transform (SRHT)
arXiv Detail & Related papers (2020-06-10T15:00:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.