Compressive Imaging: A New Single Pixel Camera
Digital Signal Processing Group
Department of Electrical and Computer Engineering
Rice University
Compressive Sensing Group
Kelly Lab
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. We have developed
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.
Camera Prototype
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Results
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| Original | 16384 Pixels 1600 Measurements (10%) | 16384 Pixels 3300 Measurements (20%) |
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| 65536 Pixels 1300 Measurements (2%) | 65536 Pixels 3300 Measurements (5%) |
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| Original | 4096 Pixels 800 Measurements (20%) | 4096 Pixels 1600 Measurements (40%) |
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|---|---|---|
| Original Object | 4096 Pixels 800 Measurements (20%) | 4096 Pixels 1600 Measurements (40%) |
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|---|---|---|
| Original Object | 4096 Pixels 800 Measurements (20%) | 4096 Pixels 1600 Measurements (40%) |
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|---|---|---|
| Original Object | 4096 Pixels 800 Measurements (20%) | 4096 Pixels 1600 Measurements (40%) |
- All pictures reconstructed using Total Variation Minimization (TV)
- Special thanks to Justin Romberg for help with TV reconstructions.
In the News...
Camera Data
Data from the CS camera is provided so that researchers can evaluate their reconstruction algorithms.
Please acknowledge the use of this data in
publications via a reference to the "Rice Single-Pixel
Camera Project, http://www.dsp.rice.edu/cscamera
Publications
- Michael Wakin, Jason Laska, Marco Duarte, Dror Baron, Shriram Sarvotham, Dharmpal Takhar, Kevin Kelly, and Richard Baraniuk, An Architecture for Compressive Imaging (Proc. International Conference on Image Processing -- ICIP 2006, Atlanta, GA, Oct. 2006)
- Michael Wakin, Jason Laska, Marco Duarte, Dror Baron, Shriram Sarvotham, Dharmpal Takhar, Kevin Kelly, and Richard Baraniuk, Compressive Imaging for Video Representation and Coding (Proc. Picture Coding Symposium -- PCS 2006, Beijing, China, Apr. 2006)
- Dharmpal Takhar, Jason Laska, Michael Wakin, Marco Duarte, Dror Baron, Shriram Sarvotham, Kevin Kelly and Richard Baraniuk, A New Compressive Imaging Camera Architecture using Optical-Domain Compression (Proc. of Computational Imaging IV at SPIE Electronic Imaging, San Jose, CA, Jan. 2006)
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