Baraniuk and Kelly, both professors of electrical and computer engineering at Rice University, have developed a camera that doesn't need to compress images. Instead, it uses a single image sensor to collect just enough information to let a novel algorithm reconstruct a high-resolution image.

At the heart of this camera is a new technique called compressive sensing. A camera using the technique needs only a small percentage of the data that today's digital cameras must collect in order to build a comparable picture. Baraniuk and Kelly's algorithm turns visual data into a handful of numbers that it randomly inserts into a giant grid. There are just enough numbers to enable the algorithm to fill in the blanks, as we do when we solve a Sudoku puzzle. When the computer solves this puzzle, it has effectively re-created the complete picture from incomplete information.

See Baraniuk's Google Tech Talk from a year ago. There's also a TED talk from later last year.

See:

D. Takhar, J. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly and R. G. 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)

Measurements vs. Bits: Compressed Sensing meets Information Theory [PPT]

Compressed sensing is a new framework for acquiring sparse signals based on the revelation that a small number of linear projections (measure- ments) of the signal contain enough information for its reconstruction. The foundation of Compressed sensing is built on the availability of noise-free measurements. However, measurement noise is unavoidable in analog systems and must be accounted for. We demonstrate that measurement noise is the crucial factor that dictates the number of measurements needed for reconstruction. To establish this result, we evaluate the information contained in the measurements by viewing the mea- surement system as an information theoretic channel. Combining the capacity of this channel with the rate- distortion function of the sparse signal, we lower bound the rate-distortion performance of a compressed sensing system. Our approach concisely captures the effect of measurement noise on the performance limits of signal reconstruction, thus enabling to benchmark the perfor- mance of specific reconstruction algorithms.

They have a whole separate site for Compressive Imaging: A New Single Pixel Camera.

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 Imaging, Reimagined | MIT Technology Review's TR10