6 papers
MG-SpaIR: Multi-grade Sparse-guided Implicit Representation for Training-Data-Free Image Restoration
Jianmin Liao, Lei Huang, Ronglong Fang +3
MG-SpaIR is a training-data-free framework for restoring a clean image from a single observation corrupted by a mixture of blur, downsampling, noise, and missing pixels. Building o…
Exact Series and Error-Controlled Evaluation of the Nonconvex Proximity Operator
Lixin Shen, Jiangyu Yu
We study the scalar proximity operator of the nonconvex power penalty with exponent between zero and one. Lagrange--Bürmann inversion gives an exact series for the larger stationar…
Signal and Image Recovery with Scale and Signed Permutation Invariant Sparsity-Promoting Functions
Jianqing Jia, Ashley Prater-Bennette, Lixin Shen
Sparse signal recovery has been a cornerstone of advancements in data processing and imaging. Recently, the squared ratio of to norms, , has be…
Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization
Qi Zhang, Yi Zhou, Ashley Prater-Bennette +2
Distributionally robust optimization (DRO) is a powerful framework for training robust models against data distribution shifts. This paper focuses on constrained DRO, which has an…
Sparse Recovery: The Square of Norms
Jianqing Jia, Ashley Prater-Bennette, Lixin Shen +1
This paper introduces a nonconvex approach for sparse signal recovery, proposing a novel model termed the -model, which utilizes the squared norms for this pur…
Computing Proximity Operators of Scale and Signed Permutation Invariant Functions
Jianqing Jia, Ashley Prater-Bennette, Lixin Shen
This paper investigates the computation of proximity operators for scale and signed permutation invariant functions. A scale-invariant function remains unchanged under uniform scal…