Title :
Stochastic modeling and estimation of multispectral image data
Author :
Schultz, Richard R. ; Stevenson, Robert L.
Author_Institution :
Dept. of Electr. Eng., Notre Dame Univ., IN, USA
Abstract :
The application of a monochromatic restoration algorithm to each channel within a multispectral image does not result in an estimate which takes into account cross-channel correlation. A non-Gaussian prior model is proposed for multispectral images, using the Gibbs distribution. The density accounts for both spatial (within channel) and spectral (between channel) information. Spatial components use a nonlinear operator to preserve discontinuities within each channel, while spectral components incorporate cross-channel information in the model. The prior density is used in a maximum a posteriori (MAP) estimation algorithm for the restoration of color images. The resulting nonlinear estimates are shown to be quantitatively superior to linear estimates produced by multichannel Wiener and least squares restoration, which implicitly use Gaussian priors
Keywords :
image restoration; maximum likelihood estimation; stochastic processes; Gibbs distribution; MAP estimation algorithm; color image restoration; cross-channel correlation; cross-channel information; density; estimation; monochromatic restoration algorithm; multispectral image data; nonGaussian prior model; nonlinear estimates; nonlinear operator; spatial information; spectral information; stochastic modeling; Color; Degradation; Frequency; Image analysis; Image restoration; Least squares methods; Multispectral imaging; Optical sensors; Stochastic processes; Wiener filter;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on
Conference_Location :
Adelaide, SA
Print_ISBN :
0-7803-1775-0
DOI :
10.1109/ICASSP.1994.389410
