Experimental Study of Super-Resolution Using a Compressive Sensing Architecture

An experimental investigation of super-resolution imaging from measurements of projections onto a random basis is presented. In particular, a laboratory imaging system was constructed following an architecture that has become familiar from the theory of compressive sensing. The system uses a digital micromirror array located at an intermediate image plane to introduce binary matrices that represent members of a basis set. The system model was developed from experimentally acquired calibration data which characterizes the system output corresponding to each individual mirror in the array. Images are reconstructed at a resolution limited by that of the micromirror array using the split Bregman approach to total-variation regularized optimization. System performance is evaluated qualitatively as a function of the size of the basis set, or equivalently, the number of snapshots applied in the reconstruction.