Downward spatially-scalable image reconstruction based on compressed sensing

According to the compressed sensing (CS) theory, we can sample a sparse signal at a rate that is (much) lower than the required Nyquist rate, while still enabling a nearly exact reconstruction. Image signals are sparse when represented in a certain domain, and because of this, a large number of CS-based image sampling and reconstruction techniques have been developed recently. In this paper, we focus on the design of the downward spatially-scalable image reconstruction from the CS-sampled data. Traditional methods usually reconstruct an image whose size is the same as the original source image and then achieve the downward scalability through sub-sampling. In our proposed method, we unify these two steps into a single one and promise to deliver a much improved quality.