Stride forward from compressed sensing

Xiteng Liu

doi:10.1117/12.2614244

31 May 2022 Stride forward from compressed sensing

Xiteng Liu

Author Affiliations +

Proceedings Volume 12097, Big Data IV: Learning, Analytics, and Applications; 120970I (2022) https://doi.org/10.1117/12.2614244
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Compressed sensing theory (CS) is the most sensational topic of scientific research in the past century. CS relies on sparse representation and L1-minimization. This reliance leads to innate weaknesses in applicability. We hereby make insightful analysis on the CS weaknesses. This is the first appearance in the literature. Based on insight into CS weaknesses, we stride forward and exemplify a new mathematical theory and method for high efficiency sensing in contrast. It remedies the CS weaknesses with radically rectified mathematical rationale, which immensely improves technical performance in terms of both data quality and computation speed. High efficiency means high quality plus high speed. The pivotal innovation is simple yet powerful. Demo software and test data are downloadable at www.lucidsee.ca.

Conference Presentation

Citation Download Citation

Xiteng Liu "Stride forward from compressed sensing", Proc. SPIE 12097, Big Data IV: Learning, Analytics, and Applications, 120970I (31 May 2022); https://doi.org/10.1117/12.2614244

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