Random projections based feature-specific structured imaging.
We present a feature-specific imaging system based on the use of structured illumination. The measurements are defined as inner products between the illumination patterns and the object reflectance function, measured on a single photodetector. The illumination patterns are defined using random binary patterns and thus do not employ prior knowledge about the object. Object estimates are generated using L(1)-norm minimization and gradient-projection sparse reconstruction algorithms. The experimental reconstructions show the feasibility of the proposed approach by using 42% fewer measurements than the object dimensionality.
Department of Electrical and Computer Engineering, 1230 East Speedway Boulevard, University of Arizona, Tucson, Arizona 85721, USA. baheti@ece.arizona.edu
Pawan K
PK
Baheti
Mark A
MA
Neifeld
D000465:Algorithms; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity