A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction
2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)(2024)
摘要
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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关键词
Hyperspectral Imaging,Compressive Sensing,Convex Algorithms,Greedy Algorithms,FISTA,ADMM,gOMP,BIHT,CoSaMP
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