A Comparative Study of Compressive Sensing Algorithms for Hyperspectral
  Imaging Reconstruction
Jon Alvarez Justo Daniela Lupu Milica Orlandic Ion Necoara Tor Arne Johansen
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.