187 citations · 705 across the 37 of their papers we have counts for
10 papers · 1 filter
Optimal Sample Complexity for Blind Gain and Phase Calibration
Yanjun Li, Kiryung Lee, Yoram Bresler
Blind gain and phase calibration (BGPC) is a structured bilinear inverse problem, which arises in many applications, including inverse rendering in computational relighting (albedo…
Blind Recovery of Sparse Signals from Subsampled Convolution
Kiryung Lee, Yanjun Li, Marius Junge +1
Subsampled blind deconvolution is the recovery of two unknown signals from samples of their convolution. To overcome the ill-posedness of this problem, solutions based on priors ta…
FRIST - Flipping and Rotation Invariant Sparsifying Transform Learning and Applications
Bihan Wen, Saiprasad Ravishankar, Yoram Bresler
Features based on sparse representation, especially using the synthesis dictionary model, have been heavily exploited in signal processing and computer vision. However, synthesis d…
Data-Driven Learning of a Union of Sparsifying Transforms Model for Blind Compressed Sensing
Saiprasad Ravishankar, Yoram Bresler
Compressed sensing is a powerful tool in applications such as magnetic resonance imaging (MRI). It enables accurate recovery of images from highly undersampled measurements by expl…
Identifiability and Stability in Blind Deconvolution under Minimal Assumptions
Yanjun Li, Kiryung Lee, Yoram Bresler
Blind deconvolution (BD) arises in many applications. Without assumptions on the signal and the filter, BD does not admit a unique solution. In practice, subspace or sparsity assum…
Identifiability in Blind Deconvolution with Subspace or Sparsity Constraints
Yanjun Li, Kiryung Lee, Yoram Bresler
Blind deconvolution (BD), the resolution of a signal and a filter given their convolution, arises in many applications. Without further constraints, BD is ill-posed. In practice, s…