309 citations · 493 across the 39 of their papers we have counts for
26 papers · 1 filter
Fast Signal Recovery from Saturated Measurements by Linear Loss and Nonconvex Penalties
Fan He, Xiaolin Huang, Yipeng Liu +1
Sign information is the key to overcoming the inevitable saturation error in compressive sensing systems, which causes information loss and results in bias. For sparse signal recov…
Compressed Sensing of Multi-Channel EEG Signals: The Simultaneous Cosparsity and Low Rank Optimization
Yipeng Liu, Maarten De Vos, Sabine Van Huffel
Goal: This paper deals with the problems that some EEG signals have no good sparse representation and single channel processing is not computationally efficient in compressed sensi…
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…
Simultaneous Greedy Analysis Pursuit for Compressive Sensing of Multi-Channel ECG Signals
Yurrit Avonds, Yipeng Liu, Sabine Van Huffel
This paper addresses compressive sensing for multi-channel ECG. Compared to the traditional sparse signal recovery approach which decomposes the signal into the product of a dictio…
Robust Cosparse Greedy Signal Reconstruction for Compressive Sensing with Multiplicative and Additive Noise
Yurrit Avonds, Yipeng Liu, Sabine Van Huffel
Greedy algorithms are popular in compressive sensing for their high computational efficiency. But the performance of current greedy algorithms can be degenerated seriously by noise…
Multi-Structural Signal Recovery for Biomedical Compressive Sensing
Yipeng Liu, Maarten De Vos, Ivan Gligorijevic +3
Compressive sensing has shown significant promise in biomedical fields. It reconstructs a signal from sub-Nyquist random linear measurements. Classical methods only exploit the spa…