Paper
22 October 1993 Adaptive, model-based restoration of textures by generalized Wiener filtering
Ravi Krishnamurthy, John W. Woods, Joseph M. Francos
Author Affiliations +
Proceedings Volume 2094, Visual Communications and Image Processing '93; (1993) https://doi.org/10.1117/12.157944
Event: Visual Communications and Image Processing '93, 1993, Cambridge, MA, United States
Abstract
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.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ravi Krishnamurthy, John W. Woods, and Joseph M. Francos "Adaptive, model-based restoration of textures by generalized Wiener filtering", Proc. SPIE 2094, Visual Communications and Image Processing '93, (22 October 1993); https://doi.org/10.1117/12.157944
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KEYWORDS
Autoregressive models

Image segmentation

Filtering (signal processing)

Electronic filtering

Image restoration

Image filtering

Signal to noise ratio

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