Minimization of for Compressed Sensing
We study minimization of the difference of \ell_1 and \ell_2 norms as a nonconvex and
Lipschitz continuous metric for solving constrained and unconstrained compressed sensing …
Lipschitz continuous metric for solving constrained and unconstrained compressed sensing …
Deephoyer: Learning sparser neural network with differentiable scale-invariant sparsity measures
In seeking for sparse and efficient neural network models, many previous works investigated
on enforcing L1 or L0 regularizers to encourage weight sparsity during training. The L0 …
on enforcing L1 or L0 regularizers to encourage weight sparsity during training. The L0 …
A weighted difference of anisotropic and isotropic total variation model for image processing
We propose a weighted difference of anisotropic and isotropic total variation (TV) as a
regularization for image processing tasks, based on the well-known TV model and natural …
regularization for image processing tasks, based on the well-known TV model and natural …
Computing Sparse Representation in a Highly Coherent Dictionary Based on Difference of and
We study analytical and numerical properties of the L_1-L_2 L 1-L 2 minimization problem
for sparse representation of a signal over a highly coherent dictionary. Though the L_1-L_2 …
for sparse representation of a signal over a highly coherent dictionary. Though the L_1-L_2 …
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. Due to the nonconvexity and nonlinearity, there has been little attention to this scale …
sparsity. Due to the nonconvexity and nonlinearity, there has been little attention to this scale …
Limited-Angle CT Reconstruction via the Minimization
In this paper, we consider minimizing the L_1/L_2 term on the gradient for a limited-angle
scanning problem in computed tomography (CT) reconstruction. We design a specific …
scanning problem in computed tomography (CT) reconstruction. We design a specific …
Accelerated Schemes for the Minimization
In this paper, we consider the L 1/L 2 minimization for sparse recovery and study its
relationship with the L 1-αL 2 model. Based on this relationship, we propose three numerical …
relationship with the L 1-αL 2 model. Based on this relationship, we propose three numerical …
Hyperspectral image denoising via global spatial-spectral total variation regularized nonconvex local low-rank tensor approximation
Hyperspectral image (HSI) denoising aims to restore clean HSI from the noise-contaminated
one which is usually caused during data acquisition and conversion. In this paper, we …
one which is usually caused during data acquisition and conversion. In this paper, we …
ℓ1− αℓ2 minimization methods for signal and image reconstruction with impulsive noise removal
In this paper, we study ℓ 1− αℓ 2 (0< α⩽ 1) minimization methods for signal and image
reconstruction with impulsive noise removal. The data fitting term is based on ℓ 1 fidelity …
reconstruction with impulsive noise removal. The data fitting term is based on ℓ 1 fidelity …
Sparse, efficient, and semantic mixture invariant training: Taming in-the-wild unsupervised sound separation
Supervised neural network training has led to significant progress on single-channel sound
separation. This approach relies on ground truth isolated sources, which precludes scaling …
separation. This approach relies on ground truth isolated sources, which precludes scaling …