430 citations · 1.6k across the 21 of their papers we have counts for
6 papers · 1 filter
Image Fusion via Sparse Regularization with Non-Convex Penalties
Nantheera Anantrasirichai, Rencheng Zheng, Ivan Selesnick +1
The L1 norm regularized least squares method is often used for finding sparse approximate solutions and is widely used in 1-D signal restoration. Basis pursuit denoising (BPD) perf…
Enhanced Low-Rank Matrix Approximation
Ankit Parekh, Ivan W. Selesnick
This letter proposes to estimate low-rank matrices by formulating a convex optimization problem with non-convex regularization. We employ parameterized non-convex penalty functions…
Convex Denoising using Non-Convex Tight Frame Regularization
Ankit Parekh, Ivan W. Selesnick
This paper considers the problem of signal denoising using a sparse tight-frame analysis prior. The L1 norm has been extensively used as a regularizer to promote sparsity; however,…
Image Restoration using Total Variation with Overlapping Group Sparsity
Jun Liu, Ting-Zhu Huang, Ivan W. Selesnick +2
Image restoration is one of the most fundamental issues in imaging science. Total variation (TV) regularization is widely used in image restoration problems for its capability to p…
Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization
Po-Yu Chen, Ivan W. Selesnick
Convex optimization with sparsity-promoting convex regularization is a standard approach for estimating sparse signals in noise. In order to promote sparsity more strongly than con…
Translation-Invariant Shrinkage/Thresholding of Group Sparse Signals
Po-Yu Chen, Ivan W. Selesnick
This paper addresses signal denoising when large-amplitude coefficients form clusters (groups). The L1-norm and other separable sparsity models do not capture the tendency of coeff…