Fusion frame, relay fusion frame and signal reconstruction

In this paper, we demonstrate that any signal exhibiting a certain sparse pattern can be recovered in a stable and resilient manner through the utilization of the fusion frame approach. The theoretical analysis highlights that the deviation of the approximate solution is effectively controlled. Furthermore, the adoption of different norms contributes to further reinforcing the guarantees of robustness and stability. Driven by the ideas of compressed sensing and fusion frames, we extend the setting to relay fusion frames. With the help of operator theory, we provide several recovery guarantee conditions based on the relay fusion frames. Finally, the relationship between relay fusion frames and compressed sensing is elucidated.

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Publication Details

Journal
Filomat
Published
2025-01-01
DOI
https://doi.org/10.2298/fil2506797h
Citations
1
Primary Topic
Sparse and Compressive Sensing Techniques
Type
article
Field-Weighted Citation Impact
1.33

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article

Fusion frame, relay fusion frame and signal reconstruction

Guoqing Hong
1 citations
Filomat
Sparse and Compressive Sensing Techniques
1.33
article

Fusion frame, relay fusion frame and signal reconstruction

Guoqing Hong
article en
1 citations

Abstract

In this paper, we demonstrate that any signal exhibiting a certain sparse pattern can be recovered in a stable and resilient manner through the utilization of the fusion frame approach. The theoretical analysis highlights that the deviation of the approximate solution is effectively controlled. Furthermore, the adoption of different norms contributes to further reinforcing the guarantees of robustness and stability. Driven by the ideas of compressed sensing and fusion frames, we extend the setting to relay fusion frames. With the help of operator theory, we provide several recovery guarantee conditions based on the relay fusion frames. Finally, the relationship between relay fusion frames and compressed sensing is elucidated.

FilomatVol. 39(6)
Henan Institute of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 20%
Sparse and Compressive Sensing Techniques
1.33
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