Experimental Study of Super-resolution Using a Compressive Sensing Architecture

March 2015
Topics: Image Processing, Sensing and Signal Processing (General), Architectures (General)
Dr. Justin C. Flake, The MITRE Corporation and Booz-Allen-Hamilton
Gary Euliss, The MITRE Corporation
John B. Greer, National Geospatial-Intelligence Agency
Stephanie Shubert, National Geospatial-Intelligence Agency
Glenn Easley, The MITRE Corporation
Kevin Gemp, The MITRE Corporation
Brian Baptista, National Geospatial-Intelligence Agency
Dr. Michael D. Stenner, The MITRE Corporation
Phil A. Sallee, Booz-Allen-Hamilton
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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.​

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