An Architecture for Compressive Imaging

Compressive sensing is an emerging field based on the rev elation 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 proto type design for the camera and include experimental results.

[1]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[2]  Calum MacAulay,et al.  Confocal microendoscopy with chromatic sectioning , 2003, SPIE BiOS.

[3]  Ronald R. Coifman,et al.  Application of spatial light modulators for new modalities in spectrometry and imaging , 2003, SPIE BiOS.

[4]  Graham Cormode,et al.  Towards an Algorithmic Theory of Compressed Sensing , 2005 .

[5]  Xiaobai Sun,et al.  Sensor-layer image compression based on the quantized cosine transform , 2005, SPIE Defense + Commercial Sensing.

[6]  David J. Brady,et al.  Compressive optical MONTAGE photography , 2005, SPIE Optics + Photonics.

[7]  J. Tropp,et al.  SIGNAL RECOVERY FROM PARTIAL INFORMATION VIA ORTHOGONAL MATCHING PURSUIT , 2005 .

[8]  E. Candès,et al.  Error correction via linear programming , 2005, FOCS 2005.

[9]  Richard G. Baraniuk,et al.  Distributed Compressed Sensing Dror , 2005 .

[10]  Richard G. Baraniuk,et al.  An Information-Theoretic Approach to Distributed Compressed Sensing ∗ , 2005 .

[11]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[12]  Richard G. Baraniuk,et al.  A new compressive imaging camera architecture using optical-domain compression , 2006, Electronic Imaging.

[13]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[14]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[15]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.