Class-Specific Interferometric Phase Estimation Using Patch-Based Importance Sampling
Figueiredo, M. A. T.
IEEE Access Vol. 8, Nº N/A, pp. 161052 - 161066, September, 2020.
ISSN (print): 2169-3536
Scimago Journal Ranking: 0,59 (in 2020)
Digital Object Identifier: 10.1109/ACCESS.2020.3021178
Interferometric phase (InPhase) estimation, that is, the denoising of modulo-2π phase images
from sinusoidal 2π-periodic and noisy observations, is a challenging inverse problem with wide applications
in many coherent imaging techniques. This paper introduces a novel approach to InPhase restoration based
on an external data set and importance sampling. In the proposed method, a class-specific data set of
clean patches is clustered using a mixture of circular symmetric Gaussian (csMoG) distributions. For each
noisy patch, a ‘‘home-cluster’’, i.e., the closest cluster in the external data set, is identified. An InPhase
estimator, termed as Shift-invariant Importance Sampling (SIS) estimator, is developed using the principles
of importance sampling. The SIS estimator uses samples from the home-cluster to perform the denoising
operation. Both the clustering mechanism and the estimation technique are developed for complex-valued
signals by taking into account patch shift invariance, which is an important property for an efficient InPhase
denoiser. The effectiveness of the proposed algorithm is shown using experiments conducted on a semi-real
InPhase data set constructed using human face images and medical imaging applications involving real
magnetic resonance imaging (MRI) data. It is observed that, in most of the experiments, the SIS estimator
shows better results compared to the state-of-the-art algorithms, yielding a minimum improvement of 1 dB
in peak signal-to-noise ratio (PSNR) for low to high noise levels.