Compressive Imaging

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.

Digital Signal Processing Group

Kelly Lab

Department of Electrical and Computer Engineering

Rice University

Compressive Sensing Resources


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
Object
4096 Pixels
800 Measurements
(20%)
4096 Pixels
1600 Measurements
(40%)


Original
Object
4096 Pixels
800 Measurements
(20%)
4096 Pixels
1600 Measurements
(40%)


Original
Object
4096 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://dsp.rice.edu/cscamera


Publications


Team Members

Faculty

Graduate Students

Alumni