Complex images contaminated by noise appear in various applications. To improve these phase images, noise effects, as loss of contrast and phase residues that deteriorate the phase unwrapping process, should be reduced. Noise reduction in complex images has been addressed by various methods, most of them dealing only with the magnitude image. 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, several de-noising algorithms in the wavelet packets domain were applied to complex images to recover the phase information. These filtering algorithms were applied to simulated 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.
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