The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
An Architecture for Compressive Imaging
Abstract
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.
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Cites
Donoho, D. L. (2004) · Cited by 16,703
Candes, E. J., Romberg, J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.862083 · Cited by 15,639
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Tropp, J. A., & Gilbert, A. C. (2007) · IEEE Transactions on Information Theory · doi:10.1109/tit.2007.909108 · Cited by 9,653
Atomic Decomposition by Basis Pursuit
Chen, S. S., Donoho, D. L., & Saunders, M. A. (1998) · SIAM Journal on Scientific Computing · doi:10.1137/s1064827596304010 · Cited by 6,845
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Candes, E. J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.885507 · Cited by 6,839
Atomic Decomposition by Basis Pursuit
Chen, S. S., Donoho, D. L., & Saunders, M. A. (2001) · SIAM Review · doi:10.1137/s003614450037906x · Cited by 5,085
A new compressive imaging camera architecture using optical-domain compression:
Takhar, D., Laska, J. N., Wakin, M. B., Duarte, M. F., Baron, D., Sarvotham, S., Kelly, K. F., & Baraniuk, R. G. (2006) · SPIE Proceedings · doi:10.1117/12.659602 · Cited by 497
Self-citationDistributed Compressed Sensing
Coluccia, G., Ravazzi, C., & Magli, E. (2015) · SpringerBriefs in Electrical and Computer Engineering · doi:10.1007/978-981-287-390-3_2 · Cited by 323
Error correction via linear programming
Candes, E., Rudelson, M., Tao, T., & Vershynin, R. (2005) · 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS'05) · doi:10.1109/sfcs.2005.5464411 · Cited by 251
Cited by
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements
Kulkarni, K., Lohit, S., Turaga, P., Kerviche, R., & Ashok, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.55 · Cited by 768
Damage Identification Scheme Based on Compressive Sensing
Wang, Y., & Hao, H. (2015) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000324 · Cited by 77
Metadata
- Authors
- Wakin, M. B., Laska, J. N., Duarte, M. F., Baron, D., Sarvotham, S., Takhar, D., Kelly, K. F., & Baraniuk, R. G.
- Venue
- 2006 International Conference on Image Processing
- Publisher
- IEEE
- Published
- 2006-10
- Pages
- 1273-1276
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2062747674
- Cited by
- 323
- References
- 9
- FWCI
- 46.58
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wakin, M. B., Laska, J. N., Duarte, M. F., Baron, D., Sarvotham, S., Takhar, D., Kelly, K. F., & Baraniuk, R. G. (2006). An Architecture for Compressive Imaging. 2006 International Conference on Image Processing. https://doi.org/10.1109/icip.2006.312577
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