A Sparsity-Based Variational Approach for the Restoration of SMOS Images From L1A Data

The Surface Moisture and Ocean Salinity (SMOS) mission senses ocean salinity and soil moisture by measuring Earth’s brightness temperature using interferometry in the L-band. These interferometry measurements known as visibilities constitute the SMOS L1A data product. Despite the L-band being reserved for Earth observation, the presence of illegal emitters causes radio frequency interference (RFI) that masks the energy radiated from the Earth and strongly corrupts the acquired images. Therefore, the recovery of brightness temperature from corrupted data by image restoration techniques is of major interest. In this paper, we propose a variational model to recover superresolved, denoised brightness temperature maps by decomposing the images into two components: an image $T$ that models the Earth’s brightness temperature and an image $O$ modeling the RFIs.