  High frame video (HFV) is an important investigational tool in sciences,
engineering and military. In ultra-high speed imaging, the obtainable temporal,
spatial and spectral resolutions are limited by the sustainable throughput of
in-camera mass memory, the lower bound of exposure time, and illumination
conditions. In order to break these bottlenecks, we propose a new coded video
acquisition framework that employs K > 2 conventional cameras, each of which
makes random measurements of the 3D video signal in both temporal and spatial
domains. For each of the K cameras, this multi-camera strategy greatly relaxes
the stringent requirements in memory speed, shutter speed, and illumination
strength. The recovery of HFV from these random measurements is posed and
solved as a large scale l1 minimization problem by exploiting joint temporal
and spatial sparsities of the 3D signal. Three coded video acquisition
techniques of varied trade offs between performance and hardware complexity are
developed: frame-wise coded acquisition, pixel-wise coded acquisition, and
column-row-wise coded acquisition. The performances of these techniques are
analyzed in relation to the sparsity of the underlying video signal.
Simulations of these new HFV capture techniques are carried out and
experimental results are reported.
