PATCH-BASED INTERFEROMETRIC PHASE ESTIMATION VIA MIXTURE OF GAUSSIAN DENSITY MODELLING IN THE COMPLEX DOMAIN
PATCH-BASED INTERFEROMETRIC PHASE ESTIMATION VIA MIXTURE OF GAUSSIAN DENSITY MODELLING IN THE COMPLEX DOMAIN, Proc Workshop on Signal Processing with Adaptive Sparse Structured Representations - SPARS, Lisbon, Portugal, Vol. , pp. - , June, 2017.
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This paper addresses interferometric phase (InPhase) image denoising; that is, the denoising of phase modulo-2π images from sinusoidal 2π-periodic and noisy observations. The wrapping discontinuities present in the InPhase images, which are to be preserved carefully, make InPhase denoising a challenging inverse problem. We tackle this problem by exploiting the self-similarity of the InPhase images. We propose a novel approach to address the problem by modelling the patches of the phase images using Mixture of Gaussian (MoG) densities in the complex domain. An Expectation Maximization (EM) algorithm is formulated to learn the parameters of the MoG from the noisy data. The learned MoG is used as a prior for estimating the InPhase images from the noisy images using Minimum Mean Square Error (MMSE) estimation. The experiments conducted on simulated and real data of InSAR/InSAS shows results which are competitive with the state-of-the-art techniques