Name
Abstract | ||
|---|---|---|
By introducing a novel spatial-spectral domain mixing prior, this paper establishes a maximum a posterior (MAP) framework for hyperspectral images (HSIs) denoising. The proposed mixing prior takes advantage of different properties of HSI in the spatial and spectral domain. Furthermore, we proposed a spatially adaptive weighted prior combining smoothing prior and discontinuity-preserving prior in the spectral domain. The weights can be defined as a function of the spectral discontinuity measure (DM). For minimizing the objective function, a half-quadratic optimization algorithm is used. The experimental results illustrate that our proposed model can get a higher signal-to-noise ratio (SNR) than using only smoothing prior or discontinuity-preserving prior. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/Geoinformatics.2012.6270354 | Geoinformatics |
Keywords | Field | DocType |
optimisation,spatially adaptive weighted prior,spectral discontinuity measure,smoothing methods,maximum a posterior framework,hyperspectral image denoising,maximum likelihood estimation,image denoising,mixing prior,half quadratic optimization algorithm,maximum a posterior (map),spectral continuity,geophysical image processing,smoothing prior,spatial-spectral domain mixing prior,hyperspectral images,discontinuity preserving prior,hypercubes,noise reduction | Noise reduction,Pattern recognition,Computer science,Discontinuity (linguistics),Maximum likelihood,Hyperspectral imaging,Smoothing,Optimization algorithm,Image denoising,Artificial intelligence,Hypercube | Conference |
ISSN | ISBN | Citations |
2161-024X | 978-1-4673-1103-8 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaolin Chen | 1 | 7 | 0.85 |
| Xiyuan Hu | 2 | 108 | 19.03 |
| S. Peng | 3 | 332 | 40.36 |