Name
Abstract | ||
|---|---|---|
By introducing a novel spatial-spectral domain mixing prior,this paper establishes a Maximum a posteriori (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 propose 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 | 10.1007/s11390-012-1269-1 | J. Comput. Sci. Technol. |
Keywords | Field | DocType |
mixing prior,spectral continuity,image denoising,hyperspectral image | Noise reduction,Pattern recognition,Computer science,Discontinuity (linguistics),Hyperspectral imaging,Smoothing,Artificial intelligence,Image denoising,Optimization algorithm,Maximum a posteriori estimation | Journal |
Volume | Issue | ISSN |
27 | 4 | 1860-4749 |
Citations | PageRank | References |
2 | 0.36 | 18 |
Authors | ||
6 | ||