181 citations · 312 across the 21 of their papers we have counts for
8 papers · 1 filter
On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework
Zeyu Yan, Fei Wen, Rendong Ying +2
Lossy compression algorithms are typically designed to achieve the lowest possible distortion at a given bit rate. However, recent studies show that pursuing high perceptual qualit…
Benefit of Joint DOA and Delay Estimation with Application to Indoor Localization in WiFi and 5G
Fei Wen, Peilin Liu, Haichao Wei +1
Accurate indoor localization has long been a challenging problem due to the presence of multipath. Joint direction-of-arrival (DOA) and time delay (TD) estimation is a promising te…
Efficient and Robust Recovery of Sparse Signal and Image Using Generalized Nonconvex Regularization
Fei Wen, Yuan Yang, Ling Pei +2
This work addresses the robust reconstruction problem of a sparse signal from compressed measurements. We propose a robust formulation for sparse reconstruction which employs the $…
Nonconvex Regularization Based Sparse Recovery and Demixing with Application to Color Image Inpainting
Fei Wen, Lasith Adhikari, Ling Pei +3
This work addresses the recovery and demixing problem of signals that are sparse in some general dictionary. Involved applications include source separation, image inpainting, supe…
Positive Definite Estimation of Large Covariance Matrix Using Generalized Nonconvex Penalties
Fei Wen, Yuan Yang, Peilin Liu +1
This work addresses the issue of large covariance matrix estimation in high-dimensional statistical analysis. Recently, improved iterative algorithms with positive-definite guarant…
Efficient Minimization Algorithms for Compressive Sensing Based on Proximity Operator
Fei Wen, Yuan Yang, Peilin Liu +2
This paper considers solving the unconstrained -norm () regularized least squares (-LS) problem for recovering sparse signals in compressive sensing. We…
Distributed Compressed Sensing off the Grid
Zhenqi Lu, Rendong Ying, Sumxin Jiang +2
This letter investigates the joint recovery of a frequency-sparse signal ensemble sharing a common frequency-sparse component from the collection of their compressed measurements.…
Spectral Compressive Sensing with Model Selection
Zhenqi Lu, Rendong Ying, Sumxin Jiang +3
The performance of existing approaches to the recovery of frequency-sparse signals from compressed measurements is limited by the coherence of required sparsity dictionaries and th…