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This paper proposes a new method of restoration and segmentation of SAR image. The radar cross-section (RCS) for intensity SAR images is estimated based on Gibbs Markov random fields and simulated annealing. Further, it puts forward to segment SAR image into target and shadow with the theory of connectivity in digital morphology. In this paper, Gibbs Markov random field models and simulated annealing...
A new method about surface feature labeling for hyperspectral images is presented in this paper in the framework of Bayesian labeling based on Markov random field (MRF). After the dimension of the hyperspectral image is reduced by PCA, a kernel density estimator and a Gaussian mixture model (GMM) are respectively used to capture the non-Gaussian statistics of the dimension-reduced images and their...
Many imaging techniques, e.g., interferometric synthetic aperture radar, magnetic resonance imaging, diffraction tomography, yield interferometric phase images. For these applications, the measurements are modulo-2p, where p is the period, a certain real number, whereas the aimed information is contained in the true phase value. The process of inferring the phase from its wrapped modulo-2p values...
Oil and gas exploration decisions are made based on inferences obtained from seismic data interpretation. The interpretation task is getting very time-consuming as seismic data sets become larger. Image processing tools such as auto-trackers assist manual interpretation of horizons-visible boundaries between certain sediment layers in seismic data. Auto-trackers assume data continuities; therefore,...
A maximum a posteriori-Markov random field (MAP-MRF) based scheme is proposed for identifying a class of shift-variant imaging systems whose point spread function can be parameterized by a single blur parameter. The space-variant (SV) blur parameter is modeled as an MRF and its MAP estimate is obtained using simulated annealing. The performance of the proposed scheme is tested on both synthetic as...
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