Tree-Structure Bayesian Compressive Sensing for Video
This addresses the problem of efficient video acquisition and reconstruction for imaging systems, but appears incremental as it builds on existing compressive sensing and Bayesian methods.
The paper tackles video reconstruction from a single monochromatic compressive measurement using a Bayesian compressive sensing framework, achieving reconstruction of up to 22 color video frames as verified with simulated and real datasets.
A Bayesian compressive sensing framework is developed for video reconstruction based on the color coded aperture compressive temporal imaging (CACTI) system. By exploiting the three dimension (3D) tree structure of the wavelet and Discrete Cosine Transformation (DCT) coefficients, a Bayesian compressive sensing inversion algorithm is derived to reconstruct (up to 22) color video frames from a single monochromatic compressive measurement. Both simulated and real datasets are adopted to verify the performance of the proposed algorithm.