Bio-inspired parameter-less heuristic for NP-hard (complete) discrete problems

Zettam, Manal

doi:10.1504/ijbic.2020.10031126

Cited by 1 publication

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“…The final result of CS is restoring the signal measured at receiver and the mathematical model of the reconstruction process is defined as a problem of underdetermined, which are usually NP‐hard 9 . Therefore, a suitable reconstruction algorithm can restore compressed signal with higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

Survey on compressed sensing reconstruction method for3Ddata

Zhang

Xie

2022

Concurrency and Computation

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The information society has higher and higher requirements for the collection, transmission and storage of digital signals, and signal utilization efficiency has become an increasingly important part of the digital signal processing process. The traditional digital signal processing needs to satisfy the Nyquist sampling theorem to ensure the restoration of the signal, while the digital signal processing method based on compressed sensing can sample and reconstruct the signal under the conditions that much lower than the Nyquist sampling theorem. Undoubtedly, the utilization efficiency of digital signals has been greatly accelerated. In this article, taking hyperspectral images and videos as examples, we review the basic theory, reconstruction model, and reconstruction algorithm of 3D data compressed sensing. We analyze, summarize, and discuss the existing literature. Finally, the research status of compressive sensing reconstruction methods for hyperspectral images and videos is compared, and several important research directions for compressive sensing reconstruction of 3D data in the future are proposed.

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Section: Introductionmentioning

confidence: 99%