Two-step spatial-temporal compressive sensing imaging

Compressed sensing(CS) technology can efficiently restore information from far fewer measurements than what Nyquist sampling theory requires. Currently, most CS reconstruction algorithms only reconstruct objects from spacial or temporal compressive measurements. Given the complexity and the difficulty, even using neural networks, it is difficult to reconstruct an object form spatial-temporal compressive measurements. In this paper, we represents the imaging process in spatial-temporal compressive imaging (STCI) into a cascaded process of spatial compressive imaging(SCI) followed by temporal compressive imaging(TCI). Thus to reconstruct an object from STCI, we first reconstruct multiple object frames from a single STCI measurement frame, and then improve object frames' resolution. The TCI reconstruction algorithm used in this paper is TwIST algorithm. To improve object frame spatial resolution, we use a deep learning network SRResNet+. Besides improving resolution, SRResNet+ can also suppress residual error in TCI reconstruction frames. We verify our idea using numerical experiments. When the compressive ratio for STCI is 16:1, or the compressive ratios for SCI and TCI both are 4:1, the reconstructions obtained using TwIST followed by SRResNet+ present a PSNR value 29dB.