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Titlebook: Mathematical Methods in Image Processing and Inverse Problems; IPIP 2018, Beijing, Xue-Cheng Tai,Suhua Wei,Haiguang Liu Conference proceed

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樓主: ominous
31#
發(fā)表于 2025-3-26 21:57:59 | 只看該作者
Multi-modality Image Registration Models and Efficient Algorithms,l to generate a diffeomorphic transformation. The idea is illustrated by using a particular model based on reformulated normalized gradients of the images as the fidelity term and higher-order derivatives as the regularizer. By adding a control term motivated by quasi-conformal maps and Beltrami coe
32#
發(fā)表于 2025-3-27 02:51:56 | 只看該作者
Fast Algorithms for Surface Reconstruction from Point Cloud,y in [Zhao, Osher, Merriman and Kang, Comp Vision and Image Under, 80(3):295–319, 2000]. An approach using Semi-Implicit Method (SIM) improves the computational efficiency through relaxation on the time-step constraint. An approach based on Augmented Lagrangian Method (ALM) reduces the run-time via
33#
發(fā)表于 2025-3-27 07:44:37 | 只看該作者
A Total Variation Regularization Method for Inverse Source Problem with Uniform Noise,tate function which is corrupted by uniform noise. Under the framework of maximum a posteriori estimator, the problem can be converted into an optimization problem where the objective function is composed of an . norm and a total variation (TV) regularization term. By introducing an auxiliary variab
34#
發(fā)表于 2025-3-27 09:40:17 | 只看該作者
35#
發(fā)表于 2025-3-27 17:15:24 | 只看該作者
36#
發(fā)表于 2025-3-27 18:52:05 | 只看該作者
On the Optimal Proximal Parameter of an ADMM-like Splitting Method for Separable Convex Programmingfunctions. Its proximal parameter is required to be sufficiently large to theoretically ensure the convergence, despite that a smaller value of this parameter is preferred for numerical acceleration. Empirically, this method has been applied to solve various applications with relaxed restrictions on
37#
發(fā)表于 2025-3-28 01:26:09 | 只看該作者
A New Initialization Method for Neural Networks with Weight Sharing, is later generalized to Kaiming initialization by He, Zhang, Ren and Sun are now widely used. However, from experiments we find that networks with heavy weight sharing are difficulty to train even with the Xavier or the Kaiming initialization. We also notice that a certain simple network can be dec
38#
發(fā)表于 2025-3-28 04:39:34 | 只看該作者
The Shortest Path AMID 3-D Polyhedral Obstacles,ent algorithm to find the globally shortest path by solving stochastic differential equations (SDEs). The main idea is based on the simple structure of the shortest path, namely it consists of straight line segments connected by junctions on the edges of the polyhedral obstacles. Thus, finding the s
39#
發(fā)表于 2025-3-28 06:29:11 | 只看該作者
40#
發(fā)表于 2025-3-28 10:57:21 | 只看該作者
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