Abstract
As a novel method to process sparse signals, compressed sensing (CS) attracts huge attention in recent years. The sensing matrix in traditional CS is completely Gaussian random, resulting in high complexity and expensive implementation cost. To solve the problem of randomness reduction, this paper investigates and applies some partially deterministic sensing matrices like Golay families, and realizes them based on several different recovery algorithms. The core procedure of structured convolutional CS is orderly selecting sparsified samples, usually in various frequency domains, then reconstructing using the same deterministic sensing matrices based on several popular recovery algorithms. Conclusions can be drawn that with these structured sensing matrices, we can get better reconstruction quality and more stable performance with less time cost.
Supported by: National Natural Science Foundation of China (61671332); Applied Basic Research Program of Wuhan City (2016010101010025); Basic Research Program of Shenzhen City (JCYJ20170306171431656); Fundamental Research Funds for the Central Universities (2042016gf0033); National Key Research and Development Program of China (2016YFB0100901).
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Wang, S., Wang, Z., Luo, Y. (2018). Structured Convolutional Compressed Sensing Based on Deterministic Subsamplers. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_38
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