Experimental Study of Super-Resolution Using a Compressive Sensing Architecture [report]

J. C. Flake, Gary Euliss, John B. Greer, Stephanie Shubert, Glenn Easley, Kevin Gemp, Brian Baptista, Michael D. Stenner, Phil A. Sallee
2015 unpublished
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
more » ... uired 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. We present a compressive sensing image system designed for super-resolution: the Adjustable Resolution Compressive Sensing Imager (ARCSI). This ARCSI sensing model is designed for the field of remote sensing, where both the sensor and the target may be moving. Compressive Sensing (CS) is a methodology recently introduced that, among other goals, strives to make efficient use of sensor resources. Due to its novelty and potential, the interest in compressive sensing has steadily risen since the seminal papers of the mid-2000s. 7, 8 CS presents entirely new ways to capture images that results in more efficient sampling and a corresponding
doi:10.21236/ada617274 fatcat:uiwdbhqtfzaupgol53ve2t5grq