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Technical Program
Paper Detail
| Paper: | TA-L6.5 |
| Session: | Signal/Image Reconstruction from Sparse Measurements |
| Time: | Tuesday, October 10, 11:20 - 11:40 |
| Presentation: |
Special Session Lecture
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| Title: |
AN ARCHITECTURE FOR COMPRESSIVE IMAGING |
| Authors: |
Michael Wakin; Rice University | | | | | Jason Laska; Rice University | | | | | Marco Duarte; Rice University | | | | | Dror Baron; Rice University | | | | | Shriram Sarvotham; Rice University | | | | | Dharmpal Takhar; Rice University | | | | | Kevin Kelly; Rice University | | | | | Richard Baraniuk; Rice University | | |
| Abstract: |
Compressive Sensing is an emerging field based on the revelation that a small group of non-adaptive linear projections of a compressible signal contains enough information for reconstruction and processing. In this paper, we propose algorithms and hardware to support a new theory of Compressive Imaging. Our approach is based on a new digital image/video camera that directly acquires random projections of the signal without first collecting the pixels/voxels. Our camera architecture employs a digital micromirror array to perform optical calculations of linear projections of an image onto pseudorandom binary patterns. Its hallmarks include the ability to obtain an image with a single detection element while measuring the image/video fewer times than the number of pixels - this can significantly reduce the computation required for video acquisition/encoding. Because our system relies on a single photon detector, it can also be adapted to image at wavelengths that are currently impossible with conventional CCD and CMOS imagers. We are currently testing a prototype design for the camera and include experimental results. |
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