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Near-Optimal Compressed Sensing Guarantees for Total Variation Minimization

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2 Author(s)
Deanna Needell ; Department of Mathematics, Claremont, CA, USA ; Rachel Ward

Consider the problem of reconstructing a multidimensional signal from an underdetermined set of measurements, as in the setting of compressed sensing. Without any additional assumptions, this problem is ill-posed. However, for signals such as natural images or movies, the minimal total variation estimate consistent with the measurements often produces a good approximation to the underlying signal, even if the number of measurements is far smaller than the ambient dimensionality. This paper extends recent reconstruction guarantees for two-dimensional images x ∈ ℂN2 to signals x ∈ ℂNd of arbitrary dimension d ≥ 2 and to isotropic total variation problems. In this paper, we show that a multidimensional signal x ∈ ℂNd can be reconstructed from O(s dlog(Nd)) linear measurements y = Ax using total variation minimization to a factor of the best s-term approximation of its gradient. The reconstruction guarantees we provide are necessarily optimal up to polynomial factors in the spatial dimension d.

Published in:

IEEE Transactions on Image Processing  (Volume:22 ,  Issue: 10 )