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Minimum Complexity Pursuit for Universal Compressed Sensing
Shirin Jalali, Arian Maleki, Richard Baraniuk
The nascent field of compressed sensing is founded on the fact that high-dimensional signals with "simple structure" can be recovered accurately from just a small number of randomi…
Minimum Complexity Pursuit: Stability Analysis
Shirin Jalali, Arian Maleki, Richard Baraniuk
A host of problems involve the recovery of structured signals from a dimensionality reduced representation such as a random projection; examples include sparse signals (compressive…
Signal Recovery on Incoherent Manifolds
Chinmay Hegde, Richard G. Baraniuk
Suppose that we observe noisy linear measurements of an unknown signal that can be modeled as the sum of two component signals, each of which arises from a nonlinear sub-manifold o…
Compressive Acquisition of Dynamic Scenes
Aswin C Sankaranarayanan, Pavan K Turaga, Rama Chellappa +1
Compressive sensing (CS) is a new approach for the acquisition and recovery of sparse signals and images that enables sampling rates significantly below the classical Nyquist rate.…