1,837 research outputs found
Fast and Efficient Compressive Sensing using Structurally Random Matrices
This paper introduces a new framework of fast and efficient sensing matrices
for practical compressive sensing, called Structurally Random Matrix (SRM). In
the proposed framework, we pre-randomize a sensing signal by scrambling its
samples or flipping its sample signs and then fast-transform the randomized
samples and finally, subsample the transform coefficients as the final sensing
measurements. SRM is highly relevant for large-scale, real-time compressive
sensing applications as it has fast computation and supports block-based
processing. In addition, we can show that SRM has theoretical sensing
performance comparable with that of completely random sensing matrices.
Numerical simulation results verify the validity of the theory as well as
illustrate the promising potentials of the proposed sensing framework
Joint Quantization and Diffusion for Compressed Sensing Measurements of Natural Images
Recent research advances have revealed the computational secrecy of the
compressed sensing (CS) paradigm. Perfect secrecy can also be achieved by
normalizing the CS measurement vector. However, these findings are established
on real measurements while digital devices can only store measurements at a
finite precision. Based on the distribution of measurements of natural images
sensed by structurally random ensemble, a joint quantization and diffusion
approach is proposed for these real-valued measurements. In this way, a
nonlinear cryptographic diffusion is intrinsically imposed on the CS process
and the overall security level is thus enhanced. Security analyses show that
the proposed scheme is able to resist known-plaintext attack while the original
CS scheme without quantization cannot. Experimental results demonstrate that
the reconstruction quality of our scheme is comparable to that of the original
one.Comment: 4 pages, 4 figure
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