Wavelet-Based Methods for Improving Signal-to-Noise Ratio in Phase Images

Abstract

Complex images with low signal to noise ratio (SNR) appear in various applications. To recover the associated phase images, noise effects, as loss of contrast and phase residues that can deteriorate the phase unwrapping process, should be reduced. There are various methods for noise filtering in complex images, however most of them deal only with the magnitude image. Only few works have been devoted to phase image de-noising, despite the existence of important applications like Interferometric Synthetic Aperture Radar (IFSAR), Current Density Imaging (CDI) and Magnetic Resonance Imaging (MRI). In this work, a group of de-noising algorithms in the wavelet domain were applied to the complex image, in order to recover the phase information. The algorithms were applied to simulated and phantom images contaminated by three different noise models, including mixtures of Gaussian and Impulsive noise. Significant improvements in SNR for low initial values (SNR<5 dB) were achieved by using the proposed filters, in comparison to other methods reported in the literature.