Adaptive basis pursuit compressive sensing reconstruction with histogram matching

In order to reconstruct missing data in very high resolution (VHR) multispectral images, several methodologies were proposed in the literature. However, missing data reconstruction still represents a complex image processing challenge to solve. A recent possibility comes from the compressive sensing (CS) theory, in particular the basis pursuit (BP) concept, which allows to find sparse signal representations in underdetermined linear equation systems. In this work, we propose an alternative selection method for the reconstruction of images adopting a histogram matching (HM) strategy. Experiments were conducted on FORMOSAT-2 images. The reported results include a simulation study and a comparison with a state-of-the-art technique for cloud removal.