Sr. Kadaba et al., RECURSIVE ESTIMATION OF IMAGES USING NON-GAUSSIAN AUTOREGRESSIVE MODELS, IEEE transactions on image processing, 7(10), 1998, pp. 1439-1452
Citations number
19
Categorie Soggetti
Computer Science Software Graphycs Programming","Computer Science Theory & Methods","Engineering, Eletrical & Electronic","Computer Science Software Graphycs Programming","Computer Science Theory & Methods
We consider recursive estimation of images modeled by non-Gaussian aut
oregressive (AR) models and corrupted by spatially white Gaussian nois
e. The goal is to find a recursive algorithm to compute a near minimum
mean square error (MMSE) estimate of each pixel of the scene using a
fixed lookahead of D rows and D columns of the observations. Our metho
d is based on a simple approximation that makes possible the developme
nt of a useful suboptimal nonlinear estimator. The algorithm is first
developed for a non-Gaussian AR time-series and then generalized to tw
o dimensions, In the process, we draw on the well-known reduced update
Kalman filter (KF) technique of Woods and Radewan [1] to circumvent c
omputational load problems, Several examples demonstrate the non-Gauss
ian nature of residuals for AR image models and that our algorithm com
pares favorably with the Kalman filtering techniques in such cases.