On the role of total variation in compressed sensing - structure dependence
It addresses the theoretical gap for non-uniform random sampling in compressed sensing, which is crucial for practical applications like MRI.
The paper provides recovery guarantees for total variation regularization in compressed sensing when Fourier samples are drawn non-uniformly, showing that the sampling set depends on the sparsity structure of the signal.
This paper considers the use of total variation regularization in the recovery of approximately gradient sparse signals from their noisy discrete Fourier samples in the context of compressed sensing. It has been observed over the last decade that a reconstruction which is robust to noise and stable to inexact sparsity can be achieved when we observe a highly incomplete subset of the Fourier samples for which the samples have been drawn in a random manner. Furthermore, in order to minimize the cardinality of the set of Fourier samples, the sampling set needs to be drawn in a non-uniform manner and the use of randomness is far more complex than the notion of uniform random sampling often considered in the theoretical results of compressed sensing. The purpose of this paper is to derive recovery guarantees in the case where the sampling set is drawn in a non-uniform random manner. We will show how the sampling set is dependent on the sparsity structure of the underlying signal.