Abstract
A Markov random field (MRF) model-based EM (expectation-maximization) procedure for simultaneously estimating the degradation model and restoring the image is described. The MRF is a coupled one which provides continuity (inside regions of smooth gray tones) and discontinuity (at region boundaries) constraints for the restoration problem which is, in general, ill posed. The computational difficulty associated with the EM procedure for MRFs is resolved by using the mean field theory from statistical mechanics. An orthonormal blur decomposition is used to reduce the chances of undesirable locally optimal estimates. Experimental results on synthetic and real-world images show that this approach provides good blur estimates and restored images. The restored images are comparable to those obtained by a Wiener filter in mean-square error, but are most visually pleasing.
Keywords
Image restorationMarkov random fieldArtificial intelligenceMarkov processComputer scienceRandom fieldImage processingMathematicsComputer visionField (mathematics)Markov chainImage (mathematics)Pattern recognition (psychology)Image segmentationStatistics
Affiliated Institutions
Related Publications
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and th...
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
This paper studies a combination of generative Markov random field (MRF) models and discriminatively trained deep convolutional neural networks (dCNNs) for synthesizing 2D image...
Fields of Experts: A Framework for Learning Image Priors
We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The app...
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
We make an analogy between images and statistical mechanics systems. Pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a ...
Shrinkage Fields for Effective Image Restoration
Many state-of-the-art image restoration approaches do not scale well to larger images, such as megapixel images common in the consumer segment. Computationally expensive optimiz...
Publication Info
- Year
- 1993
- Type
- article
- Volume
- 2
- Issue
- 1
- Pages
- 27-40
- Citations
- 148
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
148
OpenAlex
Cite This
J. Zhang
(1993).
The mean field theory in EM procedures for blind Markov random field image restoration.
IEEE Transactions on Image Processing
, 2
(1)
, 27-40.
https://doi.org/10.1109/83.210863
Identifiers
- DOI
- 10.1109/83.210863