2 May 2012 Image reconstruction and target acquisition through compressive sensing
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Abstract
Compressive imaging is an emerging field which allows one to acquire far fewer measurements of a scene than a standard pixel array and still retain the information contained in the scene. One can use these measurements to reconstruct the original image or even a processed version of the image. Recent work in compressive imaging from random convolutions is extended by relaxing some model assumptions and introducing the latest sparse reconstruction algorithms. We then compare image reconstruction quality of various convolution mask sizes, compression ratios, and reconstruction algorithms. We also expand the algorithm to derive a pattern recognition system which operates of a compressively sensed measurement stream. The developed compressive pattern recognition system reconstructions the detections map of the scene without the intermediate step of image reconstruction. A case study is presented where pattern recognition performance of this compressive system is compared against a full resolution image.
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Robert Muise, Robert Muise, Matthew Suttinger, Matthew Suttinger, } "Image reconstruction and target acquisition through compressive sensing", Proc. SPIE 8391, Automatic Target Recognition XXII, 83910O (2 May 2012); doi: 10.1117/12.918656; https://doi.org/10.1117/12.918656
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