7 citations · 17 across the 14 of their papers we have counts for
5 papers · 1 filter
Performance Analysis of Joint-Sparse Recovery from Multiple Measurements and Prior Information via Convex Optimization
Shih-Wei Hu, Gang-Xuan Lin, Sung-Hsien Hsieh +2
We address the problem of compressed sensing with multiple measurement vectors associated with prior information in order to better reconstruct an original sparse matrix signal. $\…
Fast Template Matching by Subsampled Circulant Matrix
Sung-Hsien Hsieh, Chun-Shien Lu, and Soo-Chang Pei
Template matching is widely used for many applications in image and signal processing and usually is time-critical. Traditional methods usually focus on how to reduce the search lo…
Fast Greedy Approaches for Compressive Sensing of Large-Scale Signals
Sung-Hsien Hsieh, Chun-Shien Lu, Soo-Chang Pei
Cost-efficient compressive sensing is challenging when facing large-scale data, {\em i.e.}, data with large sizes. Conventional compressive sensing methods for large-scale data wil…
Compressive Sensing of Large-Scale Images: An Assumption-Free Approach
Wei-Jie Liang, Gang-Xuan Lin, Chun-Shien Lu
Cost-efficient compressive sensing of big media data with fast reconstructed high-quality results is very challenging. In this paper, we propose a new large-scale image compressive…
The Necessary And Sufficient Condition for Generalized Demixing
Chun-Yen Kuo, Gang-Xuan Lin, Chun-Shien Lu
Demixing is the problem of identifying multiple structured signals from a superimposed observation. This work analyzes a general framework, based on convex optimization, for solvin…