Title

Image Reconstruction And Target Acquisition Through Compressive Sensing

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. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

Publication Date

6-28-2012

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering

Volume

8391

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1117/12.918656

Socpus ID

84862661135 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84862661135

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