5 citations · 8 across the 3 of their papers we have counts for
3 papers
stat.ML2016★ 1 cited
Robust Bayesian Compressed sensing
Qian Wan, Huiping Duan, Jun Fang +1
We consider the problem of robust compressed sensing whose objective is to recover a high-dimensional sparse signal from compressed measurements corrupted by outliers. A new sparse…
cs.IT2015★ 2 cited
Computationally Efficient Sparse Bayesian Learning via Generalized Approximate Message Passing
Fuwei Li, Jun Fang, Huiping Duan +2
The sparse Beyesian learning (also referred to as Bayesian compressed sensing) algorithm is one of the most popular approaches for sparse signal recovery, and has demonstrated supe…
cs.IT2014★ 5 cited
Super-Resolution Compressed Sensing: A Generalized Iterative Reweighted L2 Approach
Jun Fang, Huiping Duan, Jing Li +2
Conventional compressed sensing theory assumes signals have sparse representations in a known, finite dictionary. Nevertheless, in many practical applications such as direction-of-…