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Compressed Imaging With a Separable Sensing Operator
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DOI：10.1109/LSP.2009.2017817.png)
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Abstract
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Compressive imaging (CI) is a natural branch of compressed sensing (CS). Although a number of CI implementations have started to appear, the design of efficient CI system still remains a challenging problem. One of the main difficulties in implementing CI is that it involves huge amounts of data, which has far-reaching implications for the complexity of the optical design, calibration, data storage and computational burden. In this paper, we solve these problems by using a two-dimensional separable sensing operator. By so doing, we reduce the complexity by factor of 10 for megapixel images. We show that applying this method requires only a reasonable amount of additional samples.
Keywords:
Compressed sensing
compressive imaging
Kronecker product
mutual coherence
separable operator
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