Adaptive, model-based restoration of textures by generalized wiener filtering

Ravi Krishnamurthy, John W. Woods, Joseph M. Francos

Research output: Contribution to journalConference articlepeer-review


We consider the adaptive restoration of inhomogeneous textured images degraded by linear blur and additive white Gaussian noise (AWGN) . 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 (MMSE) estimates for fields with discontinuous spectral distributions. Texture discrimination is performed by obtaining maximum a posieriori (MAP) estimates for the label field using simulated annealing. The performance of our segmentation algorithm is investigated in the presence of noise.

Original languageEnglish
Pages (from-to)261-268
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 1 Dec 1993
Externally publishedYes
EventVisual Communications and Image Processing 1993 - Cambridge, MA, United States
Duration: 7 Nov 19937 Nov 1993

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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