Compressed sensing and Bayesian experimental design
Proceedings of the 25th international conference on Machine learning
Dense Fast Random Projections and Lean Walsh Transforms
APPROX '08 / RANDOM '08 Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques
Compressive light transport sensing
ACM Transactions on Graphics (TOG)
IEEE Transactions on Image Processing
Practical compressive sensing of large images
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Image representation by compressive sensing for visual sensor networks
Journal of Visual Communication and Image Representation
Informative sensing of natural images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Signal Processing
Variable density compressed image sampling
IEEE Transactions on Image Processing
Optimized projection matrix for compressive sensing
EURASIP Journal on Advances in Signal Processing
Face classification via sparse approximation
BioID'11 Proceedings of the COST 2101 European conference on Biometrics and ID management
Compressive sensing of object-signature
OSC'10 Proceedings of the Third international conference on Optical supercomputing
High-resolution ranging method based on low-rate parallel random sampling
Digital Signal Processing
Anomaly detection in large-scale data stream networks
Data Mining and Knowledge Discovery
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Compressed sensing (CS) offers a joint compression and sensing processes, based on the existence of a sparse representation of the treated signal and a set of projected measurements. Work on CS thus far typically assumes that the projections are drawn at random. In this paper, we consider the optimization of these projections. Since such a direct optimization is prohibitive, we target an average measure of the mutual coherence of the effective dictionary, and demonstrate that this leads to better CS reconstruction performance. Both the basis pursuit (BP) and the orthogonal matching pursuit (OMP) are shown to benefit from the newly designed projections, with a reduction of the error rate by a factor of 10 and beyond.