Insights into prior learning for despeckling SAR images

Abstract

This study presents an exploratory comparative study for learning the priors of synthetic aperture radar (SAR) images, using the Fields of Experts approach, a filter-based higher-order Markov random fields model, which has substantially improved the learning capability of the entire image priors. First, the authors provide new insights into prior learning for despeckling SAR images, which commonly exhibits a variety of the likelihood functions due to the complex physical process of the scattering. Second, the authors introduce a prior learning framework using a bi-level optimisation algorithm. Third, some interesting experiments are conducted to learn the priors of SAR images. Finally, the authors validate the learned priors on despeckling SAR images. It suggests that the representation of the prior is more accurate by using the training samples from the filtered real SAR images themselves than from both the optical images of the Earth's surface and the natural images.