@article {177, title = {Quasi-Monte Carlo estimation approach for denoising MRI data based on regional statistics}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {58}, year = {2011}, month = {1076 - 1083}, abstract = {

An important postprocessing step for MR data is noise reduction. Noise in MR data is difficult to suppress due to its signal-dependence. To address this issue, a novel stochastic approach to noise reduction for MR data is presented. The estimation of the noise-free signal is formulated as a general Bayesian least-squares estimation problem and solved using a quasi-Monte Carlo method that takes into account the statistical characteristics of the underlying noise and the regional statistics of the observed signal in a data-adaptive manner. A set of experiments were per formed to compare the proposed quasi-Monte Carlo estimation (QMCE) method to state-of-the-art wavelet-based MR noise reduction (WAVE) and nonlocal means MR noise reduction (NLM) methods using MR data volumes with synthetic noise, as well as real noise-contaminated MR data. Experimental results show that QMCE is capable of achieving state-of-the-art performance when compared to WAVE and NLM methods quantitatively in SNR, mean structural similarity (MSSIM), and contrast measures. Visual comparisons show that QMCE provides effective noise suppression, while better preserving tissue structural boundaries and restoring contrast.

}, keywords = {Bayes methods, biomedical MRI, contrast measures, data adaptive method, general Bayesian least-squares estimation, Image Denoising, mean structural similarity, medical image processing, Monte Carlo methods, MR data post processing step, noise-free signal, nonlocal means MR noise reduction, QMCE, quasi Monte Carlo estimation approach, regional statistics, stochastic approach, stochastic processes, tissue structural boundaries, WAVE, wavelet transforms, wavelet-based MR noise reduction}, issn = {0018-9294}, doi = {http://dx.doi.org/10.1109/TBME.2010.2048325}, author = {A Wong and A Mishra} } @article {176, title = {Quasi-Monte Carlo estimation approach for denoising MRI data based on regional statistics}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {58}, year = {2011}, month = {1076 - 1083}, abstract = {

An important postprocessing step for MR data is noise reduction. Noise in MR data is difficult to suppress due to its signal-dependence. To address this issue, a novel stochastic approach to noise reduction for MR data is presented. The estimation of the noise-free signal is formulated as a general Bayesian least-squares estimation problem and solved using a quasi-Monte Carlo method that takes into account the statistical characteristics of the underlying noise and the regional statistics of the observed signal in a data-adaptive manner. A set of experiments were per formed to compare the proposed quasi-Monte Carlo estimation (QMCE) method to state-of-the-art wavelet-based MR noise reduction (WAVE) and nonlocal means MR noise reduction (NLM) methods using MR data volumes with synthetic noise, as well as real noise-contaminated MR data. Experimental results show that QMCE is capable of achieving state-of-the-art performance when compared to WAVE and NLM methods quantitatively in SNR, mean structural similarity (MSSIM), and contrast measures. Visual comparisons show that QMCE provides effective noise suppression, while better preserving tissue structural boundaries and restoring contrast.

}, keywords = {Bayes methods, biomedical MRI, contrast measures, data adaptive method, general Bayesian least-squares estimation, Image Denoising, mean structural similarity, medical image processing, Monte Carlo methods, MR data post processing step, noise-free signal, nonlocal means MR noise reduction, QMCE, quasi Monte Carlo estimation approach, regional statistics, stochastic approach, stochastic processes, tissue structural boundaries, WAVE, wavelet transforms, wavelet-based MR noise reduction}, issn = {0018-9294}, doi = {http://dx.doi.org/10.1109/TBME.2010.2048325}, author = {A Wong and A Mishra} } @inproceedings {257, title = {Spectral variation constrained power spectral density estimation for wideband spectrum sensing}, booktitle = {3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL)}, year = {2010}, abstract = {

In this paper, a novel power spectrum density (PSD) estimation approach is proposed for accurate and efficient wideband spectrum sensing in Cognitive Radio (CR) systems. Based on the observed signal from a wideband receiver, the goal of determining the fluctuation-free signal PSD is formulated as a constrained Bayesian estimation problem, subject to spectral variation constraints between neighboring spectral frequencies. The extracted signal PSD obtained using the proposed approach can then used in the energy detection process to make informed decisions with regards to the identification of free spectrum resources for opportunistic access by the CR. Experimental results using Monte Carlo simulations and real terrestrial digital TV (DTV) signal acquisitions show that the proposed approach allows for accurate PSD computation using wideband receivers under unknown noise and fluctuation conditions. Therefore, there is great potential for integrating the proposed method into existing energy detection methods for more accurate and efficient wideband spectrum sensing in CR systems under unknown noise and channel conditions.

}, keywords = {Bayes methods, cognitive radio, cognitive radio system, constrained Bayesian estimation problem, CR systems, digital television, energy detection process, fluctuation-free signal PSD, Monte Carlo methods, Monte Carlo simulations, neighboring spectral frequency, power spectrum density estimation approach, radio receivers, real terrestrial digital TV signal acquisitions, signal detection, spectral analysis, spectral variation constraint, wideband receiver, wideband spectrum sensing}, doi = {http://dx.doi.org/10.1109/ISABEL.2010.5702780}, author = {X Wang and A Wong and S-Y Lien} } @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 = {

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.

}, 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} } @article {357, title = {SAR sea-ice image analysis based on iterative region growing using semantics}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {45}, year = {2007}, pages = {3919 - 3931}, abstract = {

Synthetic aperture radar (SAR) has been intensively used for sea-ice monitoring in polar regions. A computer-assisted analysis of SAR sea-ice imagery is extremely difficult due to numerous imaging parameters and environmental factors. This paper presents a system which, with some limited information provided, is able to perform an automated segmentation and classification for the SAR sea-ice imagery. In the system, both the segmentation and classification processes are based on a Markov random-field theory and are formulated in a joint manner under the Bayesian framework. Solutions to the formulation are obtained by a region-growing technique which keeps refining the segmentation and producing semantic class labels at the same time in an iterative manner. The algorithm is a general-segmentation approach named iterative region growing using semantics, which, in this paper, is dedicated to the problem of classifying the operational SAR sea-ice imagery provided by the Canadian Ice Service (CIS). The classified image results have been validated by the CIS personnel, and the resulting classifications are quite successful using the same algorithm applied to diverse data sets.

}, keywords = {automated segmentation, Bayes methods, Bayesian framework, Canadian Ice Service, iterative region growing technique, Markov random-field theory, oceanographic techniques, polar regions, remote sensing by radar, sea ice, sea-ice image analysis, sea-ice monitoring, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/TGRS.2007.908876}, author = {Q Yu and D A. Clausi} } @inproceedings {395, title = {A combined Bayesshrink wavelet-ridgelet technique for image denoising}, booktitle = {2006 IEEE International Conference on Multimedia and Expo}, year = {2006}, abstract = {

In this paper a combined Bayesshrink wavelet-ridgelet de-noising method is presented. In our previous work we have showed that Bayesshrink ridgelet performs better than Visushrink ridgelet and Visushrink wavelet. Although our Bayesshrink ridgelet technique performs somewhat poorer in comparison with Bayesshrink wavelet, based on SNR, visually it produces smoother results, especially for images with straight lines. In the proposed method Bayesshrink wavelet is combined with Bayesshrink ridgelet denoising method which performs better than each filter individually. The proposed combined denoising method gains the advantage of each filter in its specific domain, i.e., wavelet for natural and ridgelet for straight regions, and produces better and smoother results, both visually and in terms of SNR

}, keywords = {Bayes methods, Bayesshrink wavelet ridgelet technique, filtering theory, Image Denoising, image denoising method, wavelet transforms}, doi = {http://dx.doi.org/10.1109/ICME.2006.262931}, author = {N Nezamoddin Kachouie and P Fieguth} } @inproceedings {394, title = {Joint image segmentation and interpretation using iterative semantic region growing on SAR sea ice imagery}, booktitle = {18th International Conference on Pattern Recognition (ICPR)}, year = {2006}, month = {08/2006}, address = {Hong Kong}, abstract = {

Segmentation of images into disjoint regions and interpretation of the regions for semantic meanings are two central tasks in an image analysis system. Typically, the segmentation and interpretation are performed separately with the interpretation as a post processing of segmentation. In this paper, we use an iterative method that keeps refining the segmentation and producing semantic class labels at the same time. The segmentation algorithm is based on a region growing technique and the interpretation is a Markov random field (MRF) based classification. The two processes are integrated under the Bayesian framework, with both aiming at reducing a defined energy. The interactions between the two are bidirectional by letting the interpretation result have some degree of control on the region growing process. Various features can hence be efficiently combined, and accurate classifications are obtained for operational synthetic aperture radar (SAR) sea ice applications

}, keywords = {Bayes methods, Bayesian framework, image analysis, image classification, image interpretation, image segmentation, iterative methods, iterative semantic region growing, Markov processes, Markov random field based classification, radar imaging, SAR sea ice imagery, sea ice, semantic class labels, synthetic aperture radar, terrain mapping}, doi = {http://dx.doi.org/10.1109/ICPR.2006.734}, author = {Q Yu and D A. Clausi} } @inproceedings {421, title = {A Gabor based technique for image denoising}, booktitle = {18th Canadian Conference on Electrical and Computer Engineering}, year = {2005}, address = {Saskatoon}, abstract = {

As an alternative to the wavelet, Gabor function has been used as an efficient representation of two dimensional signals. We are interested in BayesShrink techniques for image denoising, and have shown in our previous work that BayesShrink Ridgelet performs better than VisuShrink ridgelet and VisuShrink wavelet. In this paper, a dyadic Gabor filter bank is combined with BayesShrink method for image denoising. In the proposed method, the noisy image is decomposed to different channels in several levels by a dyadic Gabor filter bank. To recover the image, the corrupting noise is removed by applying the proposed BayesShrink method on the noisy Gabor coefficients. The noise variance is estimated in Gabor domain and the estimated noise is then used to dynamically calculate an individual threshold for each spatio-frequency channel. Finally denoised coefficients are transformed back to reconstruct the image

}, keywords = {Bayes methods, BayesShrink ridgelet, channel bank filters, dyadic Gabor filter banks, Gabor based technique, Gabor filters, Image Denoising, image reconstruction, image representation, noise variance, spatiofrequency channels, two-dimensional signal representation, VisuShrink ridgelet, wavelet transforms}, doi = {http://dx.doi.org/10.1109/CCECE.2005.1557140}, author = {N Nexamoddin Kachouie and P Fieguth} } @inproceedings {533, title = {Modeling the correlation structure of images in the wavelet domain}, booktitle = {14th Canadian Conference on Electrical and Computer Engineering}, year = {2001}, address = {Toronto}, abstract = {

In this paper we investigate the correlation structure of the wavelet coefficients corresponding to random fields. The context of this work is the study of Bayesian approaches to wavelet shrinkage for the purposes of image denoising. This paper concentrates on both within-scale and across-scale statistical dependencies for a variety of wavelets and random fields, with examples provided for both 1-D and 2-D signals. The results show the whitening effect of the wavelet transform to be quite clear-even for particular highly correlated spatial processes the within-scale correlation decays exponentially fast, however the correlation between scales is surprisingly substantial, even for separations several scales apart. Our goal, initiated in this paper, is the development of an efficient random field model, describing these statistical correlations, and the demonstration of its effectiveness in the context of Bayesian wavelet shrinkage for signal and image denoising

}, keywords = {1-D signals, 2-D signals, across-scale statistical dependencies, Bayes methods, Bayesian approaches, correlation structure, correlation theory, Image Denoising, image enhancement, random field model, random fields, wavelet coefficients, wavelet domain, wavelet shrinkage, wavelet transforms, white noise, whitening effect, within-scale statistical dependencies}, doi = {http://dx.doi.org/10.1109/CCECE.2001.933599}, author = {Z Azimifar and P Fieguth and E Jernigan} } @inproceedings {567, title = {Sea ice segmentation using Markov random fields}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {2001}, abstract = {

Tools are required to assist the identification of pertinent classes in SAR sea ice imagery. Texture models offer a means of performing this task. The texture information in SAR sea ice imagery can be characterized by two Markov random field models: the Gauss model for conditional distribution of the observed intensity image and the discrete model for the underlying texture label image. The segmentation can be implemented as an optimization process of maximizing a posteriori distribution in a Bayesian framework

}, keywords = {Bayes methods, Bayesian framework, classes, conditional distribution, discrete model, geophysical signal processing, identification, image classification, image segmentation, image texture, intensity image, Markov processes, Markov Random Field models, maximizing a posteriori distribution, maximum likelihood estimation, oceanographic techniques, optimisation, optimization process, radar imaging, remote sensing by radar, SAR sea ice imagery, sea ice, Segmentation, synthetic aperture radar, texture models, underlying texture label image}, doi = {http://dx.doi.org/10.1109/IGARSS.2001.977102}, author = {B Yue and D A. Clausi} } @inproceedings {568, title = {Wavelet shrinkage with correlated wavelet coefficients}, booktitle = {International Conference on Image Processing}, year = {2001}, address = {Greece}, abstract = {

This paper investigates the statistical characterization of multiscale wavelet coefficients corresponding to random signals and images. Virtually all approaches to wavelet shrinkage model the wavelet coefficients as independent; we challenge that assumption and demonstrate several cases where substantial correlations may be present in the wavelet domain. In particular, the correlation between scales can be surprisingly substantial, even for pixels separated by several scales. Our goal is to develop an efficient random field model describing these statistical correlations, and demonstrate its effectiveness in the context of Bayesian wavelet shrinkage for signal and image denoising

}, keywords = {Bayes methods, Bayesian wavelet shrinkage, correlated wavelet coefficients, Image Denoising, image resolution, multiscale wavelet coefficients, random field model, signal denoising, smoothing methods, statistical analysis, statistical correlations, wavelet transforms}, doi = {http://dx.doi.org/10.1109/ICIP.2001.958076}, author = {Z Azimifar and P Fieguth and E Jernigan} }