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A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction

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arxiv 2401.14762 v1 pith:I37TOTAW submitted 2024-01-26 cs.CV

Jon Alvarez Justo , Daniela Lupu , Milica Orlandic , Ion Necoara , Tor Arne Johansen This is my paper · ORCID
classification cs.CV
keywords dataalgorithmshyperspectralimagingrecoveryaccuracycomparativecompressed
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Hyperspectral Imaging comprises excessive data consequently leading to significant challenges for data processing, storage and transmission. Compressive Sensing has been used in the field of Hyperspectral Imaging as a technique to compress the large amount of data. This work addresses the recovery of hyperspectral images 2.5x compressed. A comparative study in terms of the accuracy and the performance of the convex FISTA/ADMM in addition to the greedy gOMP/BIHT/CoSaMP recovery algorithms is presented. The results indicate that the algorithms recover successfully the compressed data, yet the gOMP algorithm achieves superior accuracy and faster recovery in comparison to the other algorithms at the expense of high dependence on unknown sparsity level of the data to recover.

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