In this paper,we propose a compound algorithm for the image restoration. The algorithm is a convex combination of the ROF model and the LET model with a parameter functionθ.The numerical experiments demonstrate that ...In this paper,we propose a compound algorithm for the image restoration. The algorithm is a convex combination of the ROF model and the LET model with a parameter functionθ.The numerical experiments demonstrate that our compound algorithm is efficient and preserves the main advantages of the two models.In particular, the errors of the compound algorithm in L2 norm between the exact images and corresponding restored images are the smallest among the three models.For images with strong noises,the restored images of the compound algorithm are the best in the corresponding restored images.The proposed algorithm combines the fixed point method, an improved AMG method and the Krylov acceleration.It is found that the combination of these methods is efficient and robust in the image restoration.展开更多
Segmentation of three-dimensional(3D) complicated structures is of great importance for many real applications.In this work we combine graph cut minimization method with a variant of the level set idea for 3D segmenta...Segmentation of three-dimensional(3D) complicated structures is of great importance for many real applications.In this work we combine graph cut minimization method with a variant of the level set idea for 3D segmentation based on the Mumford-Shah model.Compared with the traditional approach for solving the Euler-Lagrange equation we do not need to solve any partial differential equations.Instead,the minimum cut on a special designed graph need to be computed.The method is tested on data with complicated structures.It is rather stable with respect to initial value and the algorithm is nearly parameter free.Experiments show that it can solve large problems much faster than traditional approaches.展开更多
Feature lines are fundamental shape descriptors and have been extensively applied to computer graphics, computer-aided design, image processing, and non-photorealistic renderingi This paper introduces a unified variat...Feature lines are fundamental shape descriptors and have been extensively applied to computer graphics, computer-aided design, image processing, and non-photorealistic renderingi This paper introduces a unified variational framework for detecting generic feature lines on polygonal meshes. The classic Mumford-Shah model is extended to surfaces. Using F-convergence method and discrete differential geometry, we discretize the proposed variational model to sequential coupled sparse linear systems. Through quadratic polyno- mials fitting, we develop a method for extracting valleys of functions defined on surfaces. Our approach provides flexible and intuitive control over the detecting procedure, and is easy to implement. Several measure functions are devised for different types of feature lines, and we apply our approach to various polygonal meshes ranging from synthetic to measured models. The experiments demonstrate both the effectiveness of our algorithms and the visual quality of results.展开更多
基金suppprt from NSFC of China,Singapore NTU project SUG 20/07,MOE Grant T207B2202NRF2007IDMIDM002-010
文摘In this paper,we propose a compound algorithm for the image restoration. The algorithm is a convex combination of the ROF model and the LET model with a parameter functionθ.The numerical experiments demonstrate that our compound algorithm is efficient and preserves the main advantages of the two models.In particular, the errors of the compound algorithm in L2 norm between the exact images and corresponding restored images are the smallest among the three models.For images with strong noises,the restored images of the compound algorithm are the best in the corresponding restored images.The proposed algorithm combines the fixed point method, an improved AMG method and the Krylov acceleration.It is found that the combination of these methods is efficient and robust in the image restoration.
基金support from the Centre for Integrated Petroleum Research(CIPR),University of Bergen, Norway,and Singapore MOE Grant T207B2202NRF2007IDMIDM002-010
文摘Segmentation of three-dimensional(3D) complicated structures is of great importance for many real applications.In this work we combine graph cut minimization method with a variant of the level set idea for 3D segmentation based on the Mumford-Shah model.Compared with the traditional approach for solving the Euler-Lagrange equation we do not need to solve any partial differential equations.Instead,the minimum cut on a special designed graph need to be computed.The method is tested on data with complicated structures.It is rather stable with respect to initial value and the algorithm is nearly parameter free.Experiments show that it can solve large problems much faster than traditional approaches.
文摘Feature lines are fundamental shape descriptors and have been extensively applied to computer graphics, computer-aided design, image processing, and non-photorealistic renderingi This paper introduces a unified variational framework for detecting generic feature lines on polygonal meshes. The classic Mumford-Shah model is extended to surfaces. Using F-convergence method and discrete differential geometry, we discretize the proposed variational model to sequential coupled sparse linear systems. Through quadratic polyno- mials fitting, we develop a method for extracting valleys of functions defined on surfaces. Our approach provides flexible and intuitive control over the detecting procedure, and is easy to implement. Several measure functions are devised for different types of feature lines, and we apply our approach to various polygonal meshes ranging from synthetic to measured models. The experiments demonstrate both the effectiveness of our algorithms and the visual quality of results.
