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Tensor Nuclear-Norm Minimization for Snapshot Compressive Imaging Cameras

Snapshot compressive imaging (SCI) cameras optically compress high-speed video frames using low-resolution sensors into one measurement frame. The key to implement efficient SCI cameras is accurate reconstruction algorithms. However, existing works are not satisfactory due to either low fidelity or extensive running time. In this paper, we propose a novel tensor nuclear-norm minimization scheme that achieves fast and accurate reconstruction for SCI cameras. First, we model the high-speed video as a low tubal-rank tensor and exploit the low tubal-rank property to reconstruct the high-speed video frames from the measurement frame. Second, we propose a novel tensor nuclear-norm minimization scheme using Alternating Direction Method of Multiplier algorithm, called TNN-ADMM. Finally, we present performance evaluations on two synthetic videos and one real SCI camera video. The propsed TNN-ADMM scheme exhibits an average improvement of 3.5 dB in PSNR over the GAP-TV algorithm, and reduces DeSCI's 8 hours' running time to less than 6 minutes.