|MAP-based denoising of hyperspectral imagery using 3-D edge-preserving priors|
|Chen Shaolin; Hu Xiyuan; Peng Silong; Shaolin Chen
|Conference Name||2012 2nd International Conference on Remote Sensing Environment and Transportation Engineering RSETE 2012
|Abstract||In the hyperspectral imaging acquired images are
inherently affected by noise whose levels may vary from band to
band. It is not a trivial task to remove this kind of noise while
preserving the edges and details of hyperspectral images (HSIs).
This paper provides a maximum a posterior (MAP)-based
denoising approach for HSIs corrupted by band-varying noise.
Compared with the classical MAP-based methods for 2-D
degraded image restoration the proposed approach uses 3-D
edge preserving priors to keep sharp edges while smoothing the
3-D HSIs. In order to adapt to the characteristics of bandvarying
noise statistics and high dynamic ranges of HSIs we adaptively
estimate the noise variance and scaling parameter of each point.
For minimizing the cost function the half-quadratic optimization
algorithm is used. Both denoising and classification experimental
results confirm the superiority and validity of the proposed
|Corresponding Author||Shaolin Chen|
Chen Shaolin,Hu Xiyuan,Peng Silong,et al. MAP-based denoising of hyperspectral imagery using 3-D edge-preserving priors[C],2012.
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