Image processing and data analysis: the multiscale approach
Image processing and data analysis: the multiscale approach
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
Multiframe selective information fusion from robust error estimation theory
IEEE Transactions on Image Processing
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
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A new fused Bayesian maximum entropy-variational analysis (BMEVA) method for enhanced radar/synthetic aperture radar (SAR) imaging is addressed as required for high-resolution remote sensing (RS) imagery. The variational analysis (VA) paradigm is adapted via incorporating the image gradient flow norm preservation into the overall reconstruction problem to control the geometrical properties of the desired solution. The metrics structure in the corresponding image representation and solution spaces is adjusted to incorporate the VA image formalism and RS model-level considerations; in particular, system calibration data and total image gradient flow power constraints. The BMEVA method aggregates the image model and system-level considerations into the fused SSP reconstruction strategy providing a regularized balance between the noise suppression and gained spatial resolution with the VA-controlled geometrical properties of the resulting solution. The efficiency of the developed enhanced radar imaging approach is illustrated through the numerical simulations with the real-world SAR imagery.