@article {418,
	title = {Statistical processing of large image sequences},
	journal = {IEEE Transactions on Image Processing},
	volume = {14},
	year = {2005},
	pages = {80 - 93},
	abstract = {<p>The dynamic estimation of large-scale stochastic image sequences, as frequently encountered in remote sensing, is important in a variety of scientific applications. However, the size of such images makes conventional dynamic estimation methods, for example, the Kalman and related filters, impractical. We present an approach that emulates the Kalman filter, but with considerably reduced computational and storage requirements. Our approach is illustrated in the context of a 512 times; 512 image sequence of ocean surface temperature. The static estimation step, the primary contribution here, uses a mixture of stationary models to accurately mimic the effect of a nonstationary prior, simplifying both computational complexity and modeling. Our approach provides an efficient, stable, positive-definite model which is consistent with the given correlation structure. Thus, the methods of this paper may find application in modeling and single-frame estimation.</p>
},
	keywords = {Algorithms, Artificial Intelligence, Automated, Biological, Cluster Analysis, computational complexity, Computer Graphics, Computer Simulation, Computer-Assisted, correlation methods, correlation structure, dynamic estimation method, image enhancement, image interpretation, image sequences, Imaging, Information Storage and Retrieval, Kalman filter, Kalman filters, large-scale stochastic image sequence estimation, Models, Movement, Numerical Analysis, ocean surface temperature, Pattern Recognition, Remote Sensing, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, single-frame estimation, static estimation step, Statistical, statistical analysis, statistical image processing, stochastic processes, Subtraction Technique, Three-Dimensional, Video Recording},
	issn = {1057-7149},
	doi = {http://dx.doi.org/10.1109/TIP.2004.838703},
	author = {F M. Khellah and P Fieguth and M J. Murray and M R. Allen}
}
@inproceedings {598,
	title = {Towards random field modeling of wavelet statistics},
	booktitle = {International Conference on Image Processing},
	year = {2002},
	address = {Rochester, NY},
	abstract = {<p>The paper investigates the statistical characterization of signals and images in the wavelet domain. In particular, in contrast to common decorrelated-coefficient models, we find that the correlation between wavelet scales can be surprisingly substantial, even across several scales. We investigate possible choices of statistical-interaction models. One efficient and fast strategy which describes the wavelet-based statistical correlations is illustrated. Finally, the effectiveness of the proposed tool towards an efficient hierarchical MRF (Markov random field) modeling of within-scale neighborhoods and across-scale dependencies is demonstrated.</p>
},
	keywords = {image characterization, image processing, Markov processes, Markov random field modeling, MRF, signal characterization, statistical analysis, statistical image processing, wavelet statistics, wavelet transforms},
	doi = {http://dx.doi.org/10.1109/ICIP.2002.1038035},
	author = {Z Azimifar and P Fieguth and E Jernigan}
}
