Hybrid Kronecker compressive sensing for images

Natural images has certain level of both similarity and difference which can be efficiently represented by deterministic and random sensing matrices in compressive sensing. In this context, a hybrid sensing matrix which combines a deterministic DCT and a random matrix, is recently investigated. In this paper, we bring the concept of hybrid sensing matrix into Kronecker compressive sensing (KCS) of images. Extensive experiment has shown that the proposed hybrid KCS method performs better than either fully random or deterministic DCT matrix, and comparatively with other state-of the-art sensing schemes in terms of reconstruction quality.