Specifically, we first show the convergence\nof the graph Laplacian regularizer to a continuous-domain functional,\nintegrating a norm measured in a locally adaptive metric space. Focusing on\nimage denoising, we derive an optimal metric space assuming non-local\nself-similarity of pixel patches, ...
Processing1GraphLaplacianRegularizationforImageDenoising:AnalysisintheContinuousDomainJiahaoPang,Member,IEEE,GeneCheung,SeniorMember,IEEEAbstract—Inverseimagingproblemsareinherentlyunder-determined,andhenceitisimportanttoemployappropriateimagepriorsforregularization.Onerecentpopularprior—thegraphLaplacianregularizer—assumesthat...
To further enhance the contrast between texture and structure, especially for visually salient regions, [Math Processing Error]L and [Math Processing Error]D are combined to form a more effective regularizer for structure-texture decomposition. The objective function is finally expressed as [Math Proce...
This is our third contribution: we give a formal boosting algorithm for the regularized exponential rado-loss which boost with any of the ridge, lasso, SLOPE, `1, or elastic net regularizer, using the same master routine for all. Because the regularized exponential rado-loss is the equivalent ...
atomicity38, and absorption39to reduce the ill-posedness of phase retrieval. Furthermore, many types of sparsity priors such as spatial domain40, gradient domain41,42, and wavelet domain43are effective regularizers for phase retrieval. Fig. 8: Description of alternating-projection algorithms....
Optical Flow Estimation using Laplacian Mesh Energy (project) Wenbin Li, Darren Cosker, Matthew Brown, Rui Tang Computationally Efficient Regression on a Dependency Graph for Human Pose Estimation (PDF, poster) Kota Hara (UMD), Rama Chellappa (UMD) Consensus of k-NNs for Robust Neighborhood Sele...
A Laplacian Framework for Option Discovery in Reinforcement Learning. Marlos C. Machado Marc G. Bellemare Michael H. Bowling 原文链接 谷歌学术 必应学术 百度学术 Interactive Learning from Policy-Dependent Human Feedback. James MacGlashan Mark K. Ho Robert Tyler Loftin Bei Peng Guan Wang ...
The matrices \(L_{x} = L_{y}\) are the 3-point graph Laplacians for the 1-D chain. We depict the true toy connectivity \(W_{\text{true}}\) as well as a number of low-rank solutions output by our method in Fig. 2. Both the mask and the regularization are required for ...
FIG. 3 is a block diagram of the hardware implementation of the stereo Laplacian image pyramids. FIG. 4 is a flow diagram of the stereo correlation algorithm. FIG. 5 is a search graph candidate array for a joint 1-D stereo matching algorithm. ...
Manifold-RegularizerPinballLossIn this paper, we consider the regularized learning schemes based on l1-regularizer and pinball loss in a data dependent hypothesis space. The target is the error analysis for the quantile regression learning. There is no regularized condition with the kernel function, ...