Amplitude vs intensity despeckling in the wavelet domain using bayesian estimators

Coherent acquisition systems, such as synthetic aperture radar (SAR), are affected by a granular disturbance named speckle. Images are usually pre-processed in order to remove speckle noise before facing further steps (dependent on the specific application) in the analysis of the data. Speckle noise is usually modelled as a multiplicative disturbance that affects either the amplitude or the intensity of an image, with different statistical properties in the two cases. Despeckling methods rely upon either the amplitude or the intensity format of the input signal. In this paper, we study the problem of despeckling SAR images when the input data is either an intensity or an amplitude signal, investigate how despeckling methods proposed for one format (e.g., intensity) can be adapted to the other format (e.g., amplitude), and analyse the performances of the algorithms in the two cases. State-of-the-art despeckling methods based on Bayesian estimators in the wavelet domain, recently proposed in the literature, are taken into consideration. Experimental results, carried out on simulated speckled images and on true SAR data, are presented and discussed in order to assess the best strategy.