A sparsity-based variational approach for the restoration of SMOS images from L1A data

The SMOS mission senses ocean salinity and soil moisture by measuring Earth's brightness temperature using in-terferometry 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 cause radio frequency interference (RFI) that mask the energy radiated from the Earth and strongly corrupt the acquired images. Therefore, the recovery of brightness temperature from corrupted data by image restoration techniques is of major interest. In this work we propose a variational model to recover super-resolved, 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. Experiments with synthetic and real data support the suitability of the proposed approach.