Compressive Sensing by Random Convolution

This paper demonstrates that convolution with random waveform followed by random time-domain subsampling is a universally efficient compressive sensing strategy. We show that an n-dimensional signal which is S-sparse in any fixed orthonormal representation can be recovered from m greater than or similar to S log n samples from its convolution with a pulse whose Fourier transform has unit magnitude and random phase at all frequencies. The time-domain subsampling can be done in one of two ways: in the first, we simply observe m samples of the random convolution; in the second, we break the random convolution into m blocks and summarize each with a single randomized sum. We also discuss several imaging applications where convolution with a random pulse allows us to superresolve fine-scale features, allowing us to recover high-resolution signals from low-resolution measurements.