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Found 11 papers, 5 papers with code
Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization
A large number of literature relies on uncertainty quantification to evaluate the reliability of a learning model, which is particularly important for applications of out-of-distribution (OOD) detection and misclassification detection.
Weighted Anisotropic-Isotropic Total Variation for Poisson Denoising
Poisson noise commonly occurs in images captured by photon-limited imaging systems such as in astronomy and medicine.
Non-convex approaches for low-rank tensor completion under tubal sampling
Tensor completion is an important problem in modern data analysis.
Difference of Anisotropic and Isotropic TV for Segmentation under Blur and Poisson Noise
In this paper, we aim to segment an image degraded by blur and Poisson noise.
A Lifted $\ell_1 $ Framework for Sparse Recovery
Motivated by re-weighted $\ell_1$ approaches for sparse recovery, we propose a lifted $\ell_1$ (LL1) regularization which is a generalized form of several popular regularizations in the literature.
An Efficient Smoothing and Thresholding Image Segmentation Framework with Weighted Anisotropic-Isotropic Total Variation
In this paper, we design an efficient, multi-stage image segmentation framework that incorporates a weighted difference of anisotropic and isotropic total variation (AITV).
Minimizing L1 over L2 norms on the gradient
Consequently, we postulate that applying L1/L2 on the gradient is better than the classic total variation (the L1 norm on the gradient) to enforce the sparsity of the image gradient.
Limited-angle CT reconstruction via the L1/L2 minimization
In this paper, we consider minimizing the L1/L2 term on the gradient for a limited-angle scanning problem in computed tomography (CT) reconstruction.
A Weighted Difference of Anisotropic and Isotropic Total Variation for Relaxed Mumford-Shah Color and Multiphase Image Segmentation
In a class of piecewise-constant image segmentation models, we propose to incorporate a weighted difference of anisotropic and isotropic total variation (AITV) to regularize the partition boundaries in an image.
Probabilistic Structure Learning for EEG/MEG Source Imaging with Hierarchical Graph Prior
Traditional EEG/MEG Source Imaging (ESI) methods usually assume that either source activity at different time points is unrelated, or that similar spatiotemporal patterns exist across an entire study period.
A Scale Invariant Approach for Sparse Signal Recovery
In this paper, we study the ratio of the $L_1 $ and $L_2 $ norms, denoted as $L_1/L_2$, to promote sparsity.
