Name
Abstract | ||
|---|---|---|
Compressive sensing (CS) is an emerging field that exploits the underlying sparsity of a signal to perform sampling at rates below the Nyquist-criterion. This article presents a new code aperture design framework for compressive spectral imaging based on the Coded Aperture Snapshot Spectral Imaging (CASSI) system. Firstly, the methodology allows the CASSI system to use multiple snapshots which permits adjustable spectral and spatial resolution. Secondly, the measurement codeword matrices are generated using a pair of model equations, leading to code aperture patterns that permit the recovery of specific spectral bands of a given object. The developed methodology is tested using a real data cube and simulations are shown which illustrate that one can recover arbitrary spectral bands with high flexibility and performance. |
Year | Venue | Keywords |
|---|---|---|
2010 | Aalborg | compressed sensing,image coding,matrix algebra,signal sampling,cassi system,code aperture design,coded aperture snapshot spectral imaging system,codeword matrix generation,compressive sensing,compressive spectral imaging,mathematical model,imaging,signal to noise ratio,apertures,image reconstruction,data models |
Field | DocType | ISSN |
Aperture,Computer vision,Full spectral imaging,Spectral imaging,Coded aperture,Computer science,Algorithm,Code word,Artificial intelligence,Spectral bands,Data cube,Compressed sensing | Conference | 2219-5491 |
Citations | PageRank | References |
2 | 0.47 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Henry Arguello | 1 | 32 | 5.39 |
| Gonzalo R. Arce | 2 | 1061 | 134.94 |