@article {221,
	title = {Generalized probabilistic scale space for image restoration},
	journal = {IEEE Transactions on Image Processing},
	volume = {19},
	year = {2010},
	pages = {2774 - 2780},
	abstract = {<p>A novel generalized sampling-based probabilistic scale space theory is proposed for image restoration. We explore extending the definition of scale space to better account for both noise and observation models, which is important for producing accurately restored images. A new class of scale-space realizations based on sampling and probability theory is introduced to realize this extended definition in the context of image restoration. Experimental results using 2-D images show that generalized sampling-based probabilistic scale-space theory can be used to produce more accurate restored images when compared with state-of-the-art scale-space formulations, particularly under situations characterized by low signal-to-noise ratios and image degradation.</p>
},
	keywords = {2D image, Bayes Theorem, Computer-Assisted, generalized sampling-based probabilistic scale space theory, Humans, image degradation, image processing, image restoration, image sampling, Male, noise model, nonlinear dynamics, Normal Distribution, Photography, probability, Prostate, signal-to-noise ratio, Ultrasonography},
	issn = {1057-7149},
	doi = {http://dx.doi.org/10.1109/TIP.2010.2048973},
	author = {A Wong and A Mishra}
}
@inproceedings {329,
	title = {Adaptive nonlinear image denoising and restoration using a cooperative Bayesian estimation approach},
	booktitle = {6th Indian Conference on Computer Vision, Graphics Image Processing},
	year = {2008},
	month = {12/2008},
	address = {Bhubaneswar, India},
	abstract = {<p>A novel nonlinear cooperative approach to image denoising and restoration is presented. Samples from the image field with similar characteristics are first grouped into clusters by first performing image decomposition based on the Mumford-Shah model using a total variational framework and performing fuzzy c-means clustering within each image partition. Samples within each cluster are then aggregated using an cooperative Bayesian estimation method based on information from all the samples to provide a nonlinear estimate of the original image. The proposed method exploits information redundancy within each cluster to denoise and restore the original image. Furthermore, the proposed cooperative Bayesian estimation method is capable of suppressing noise and reducing image degradation while preserving image detail by utilizing intra-cluster statistics. The experimental results using different types of images demonstrate that the proposed algorithm provides state-of-the-art image denoising performance in terms of both peak signal-to-noise ratio (PSNR) and subjective visual quality.</p>
},
	keywords = {adaptive nonlinear image denoising, Bayes methods, cooperative Bayesian estimation, fuzzy c-means clustering, fuzzy set theory, image decomposition, Image Denoising, image restoration, intracluster statistics, Mumford-Shah model, nonlinear cooperative approach, pattern clustering, peak signal-to-noise ratio, subjective visual quality},
	doi = {http://dx.doi.org/10.1109/ICVGIP.2008.28},
	author = {A Mishra and A Wong and D A. Clausi and P Fieguth}
}
