Title
Gpu Implementation Of A Hyperspectral Coded Aperture Algorithm For Compressive Sensing
Abstract
This paper presents a new parallel implementation of a previously hyperspectral coded aperture (HYCA) algorithm for compressive sensing on graphics processing units (GPUs). HYCA method combines the ideas of spectral unmixing and compressive sensing exploiting the high spatial correlation that can be observed in the data and the generally low number of endmembers needed in order to explain the data. The proposed implementation exploits the GPU architecture at low level, thus taking full advantage of the computational power of GPUs using shared memory and coalesced accesses to memory. The proposed algorithm is evaluated not only in terms of reconstruction error but also in terms of computational performance using two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN. Experimental results using real data reveals signficant speedups up with regards to serial implementation.
Year
Venue
Keywords
2015
2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
Hyperspectral imaging, compressive sensing (CS), coded aperture, graphics processing units (GPUS)
Field
DocType
ISSN
Aperture,Iterative reconstruction,Graphics,Computer vision,Spatial correlation,Coded aperture,Shared memory,Computer science,Algorithm,Hyperspectral imaging,Artificial intelligence,Compressed sensing
Conference
2153-6996
Citations 
PageRank 
References 
2
0.43
8
Authors
6
Name
Order
Citations
PageRank
Sergio Bernabe113512.45
Gabriel Martin2695.35
José M. P. Nascimento3120166.79
José M. Bioucas-Dias43565173.67
Antonio Plaza53475262.63
V. Silva68013.53