Title :
Adaptive image restoration using a generalized Gaussian model for unknown noise
Author :
Pun, Wai Ho ; Jeffs, Brian D.
Author_Institution :
Brigham Young Univ., Provo, UT, USA
fDate :
10/1/1995 12:00:00 AM
Abstract :
A model adaptive method is proposed for restoring blurred and noise corrupted images. The generalized p-Gaussian family of probability density functions is used as the approximating parametric noise model. Distribution shape parameters are estimated from the image, and the resulting maximum likelihood optimization problem is solved. An iterative algorithm for data-directed restoration is presented and analyzed
Keywords :
Gaussian distribution; Gaussian processes; adaptive signal processing; image restoration; iterative methods; maximum likelihood estimation; noise; optimisation; adaptive image restoration; approximation; blurred images; data-directed restoration; distribution shape parameters; generalized Gaussian model; iterative algorithm; maximum likelihood optimization problem; noise corrupted images; parameter estimation; parametric noise model; probability density functions; unknown noise; Degradation; Gaussian noise; Image edge detection; Image restoration; Iterative algorithms; Least squares methods; Maximum likelihood estimation; Noise shaping; Shape; Vectors;
Journal_Title :
Image Processing, IEEE Transactions on
