Recovery guarantees for TV regularized compressed sensing

This paper considers the problem of recovering a one or two dimensional discrete signal which is approximately sparse in its gradient from an incomplete subset of its Fourier coefficients which have been corrupted with noise. The results show that in order to obtain a reconstruction which is robust to noise and stable to inexact gradient sparsity of order s with high probability, it suffices to draw O(s log N) of the available Fourier coefficients uniformly at random. However, if one draws O(s log N) samples in accordance to a particular distribution which concentrates on the low Fourier frequencies, then the stability bounds which can be guaranteed are optimal up to log factors. The final result of this paper shows that in the one dimensional case where the underlying signal is gradient sparse and its sparsity pattern satisfies a minimum separation condition, then to guarantee exact recovery with high probability, for some M <; N, it suffices to draw O(s log M logs) samples uniformly at random from the Fourier coefficients whose frequencies are no greater than M.