Dictionary Learning With Optimized Projection Design for Compressive Sensing Applications

In this letter, we propose a new method for the joint design of both the projections and the sparsifying dictionary in order to improve signal reconstruction performance in compressive sensing (CS) applications. By capitalizing on the optimized projection matrix design in , which admits a closed-form expression as a function of any overcomplete dictionary, the proposed method does not need to involve directly the projection matrix. The projection matrix of our joint design can be directly derived based on the learned dictionary. Simulation results show that our joint design framework, which is constituted based on a set of training image patches, leads to an improved reconstruction performance in comparison to other recent approaches.