Compressive imaging systems with spatial-temporal encoding can be used to capture and reconstruct fast-moving objects.
The imaging quality highly depends on the choice of encoding masks and reconstruction methods. In this paper, we present a new
network architecture to jointly design the encoding masks and the reconstruction method for compressive high-frame-rate imaging.
Unlike previous works, the proposed method takes full advantage of denoising prior to provide a promising frame reconstruction. The
network is also flexible enough to optimize full-resolution masks and efficient at reconstructing frames. To this end, we develop a new
dense network architecture that embeds Anderson acceleration, known from numerical optimization, directly into the neural network
Our experiments show the optimized masks and the dense accelerated network respectively achieve 1.5 dB and 1 dB improvements
in PSNR without adding training parameters. The proposed method outperforms other state-of-the-art methods both in simulations and
on real hardware. In addition, we set up a coded two-bucket camera for compressive high-frame-rate imaging, which is robust to imaging
noise and provides promising results when recovering nearly 1,000 frames per second.