716 citations · 2.8k across the 74 of their papers we have counts for
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Unified Theory for Recovery of Sparse Signals in a General Transform Domain
Kiryung Lee, Yanjun Li, Kyong Hwan Jin +1
Compressed sensing provided a data-acquisition paradigm for sparse signals. Remarkably, it has been shown that practical algorithms provide robust recovery from noisy linear measur…
Compressive Sampling using Annihilating Filter-based Low-Rank Interpolation
Jong Chul Ye, Jong Min Kim, Kyong Hwan Jin +1
While the recent theory of compressed sensing provides an opportunity to overcome the Nyquist limit in recovering sparse signals, a solution approach usually takes a form of invers…
A general framework for compressed sensing and parallel MRI using annihilating filter based low-rank Hankel matrix
Kyong Hwan Jin, Dongwook Lee, Jong Chul Ye
Parallel MRI (pMRI) and compressed sensing MRI (CS-MRI) have been considered as two distinct reconstruction problems. Inspired by recent k-space interpolation methods, an annihilat…
Improving M-SBL for Joint Sparse Recovery using a Subspace Penalty
Jong Chul Ye, Jong Min Kim, Yoram Bresler
The multiple measurement vector problem (MMV) is a generalization of the compressed sensing problem that addresses the recovery of a set of jointly sparse signal vectors. One of th…
Improving Noise Robustness in Subspace-based Joint Sparse Recovery
Jong Min Kim, Ok Kyun Lee, Jong Chul Ye
In a multiple measurement vector problem (MMV), where multiple signals share a common sparse support and are sampled by a common sensing matrix, we can expect joint sparsity to ena…
Exact Dynamic Support Tracking with Multiple Measurement Vectors using Compressive MUSIC
Jong Min Kim, Ok Kyun Lee, Jong Chul Ye
Dynamic tracking of sparse targets has been one of the important topics in array signal processing. Recently, compressed sensing (CS) approaches have been extensively investigated…