Compressive Sensing is an emerging field based on the revelation that a small group of non-adaptive linear projections of a compressible signal or image contains enough information for reconstruction and processing. Our new digital image/video camera directly acquires random projections of a scene without first collecting the pixels/voxels. The camera architecture employs a digital micromirror array to optically calculate linear projections of the scene onto pseudorandom binary patterns. Its key hallmark is its ability to obtain an image or video with a single detection element (the "single pixel") while measuring the scene fewer times than the number of pixels/voxels. Since the camera relies on a single photon detector, it can also be adapted to image at wavelengths where conventional CCD and CMOS imagers are blind.
Camera Prototype
Results
Original 16384 Pixels
1600 Measurements
(10%)16384 Pixels
3300 Measurements
(20%)
65536 Pixels
1300 Measurements
(2%)65536 Pixels
3300 Measurements
(5%)
Original 4096 Pixels
800 Measurements
(20%)4096 Pixels
1600 Measurements
(40%)65536 Pixels
6600 Measurements
(10%)
Original
Object4096 Pixels
800 Measurements
(20%)4096 Pixels
1600 Measurements
(40%)
Original
Object4096 Pixels
800 Measurements
(20%)4096 Pixels
1600 Measurements
(40%)
Original
Object4096 Pixels
800 Measurements
(20%)4096 Pixels
1600 Measurements
(40%)
- All images reconstructed using Total Variation Minimization (TV)
- Special thanks to Justin Romberg for help with TV reconstructions.
Rice Single-Pixel Camera 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)
- Mark Davenport, Marco Duarte, Michael Wakin, Jason Laska, Dharmpal Takhar, Kevin Kelly, and Richard Baraniuk, The Smashed Filter for Compressive Classification and Target Recognition Proc of Computational IMaging V at SPIE Electronic Imaging, San Jose, California, January 2007.
- Marco F. Duarte, Mark A. Davenport, Dharmpal Takhar, Jason N. Laska, Ting Sun, Kevin F. Kelly and Richard G. Baraniuk, Single Pixel Imaging via Compressive Sampling, IEEE Signal Processing Magazine, March 2008. To appear.
Team Members
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