Adaptive model-based restoration of textures by generalized Wiener filtering

摘要

We consider the adaptive restoration of inhomogeneous textured images degraded by linear blur and additive white Gaussian noise. The method consists of segmenting the image into individual homogeneous textures and restoring each texture separately. The individual textures are assumed to be realizations of 2-D Wold-decomposition based regular, homogeneous random fields which may possess deterministic components. The conventional Wiener filter assumes that the spectral distribution of the signal is absolutely continuous and, therefore, cannot be directly used to restore the individual textures. A generalized Wiener filter accommodates the unified texture model and is shown to yield minimum mean-squared error estimates for fields with discontinuous spectral distributions. Texture discrimination is performed by obtaining maximum a posteriori estimates for the label field using simulated annealing. The performance of our segmentation algorithm is investigated in the presence of noise.