Compressive imaging measurement design from an image patch manifold prior

Robert Muise; Dave Bottisti

doi:10.1117/12.919659

4 May 2012 Compressive imaging measurement design from an image patch manifold prior

Robert Muise, Dave Bottisti

Proceedings Volume 8399, Visual Information Processing XXI; 839905 (2012) https://doi.org/10.1117/12.919659
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

We look at the design of projective measurements based upon image priors. If one assumes that image patches from natural imagery can be modeled as a low rank manifold, we develop an optimality criterion for a measurement matrix based upon separating the canonical elements of the manifold prior. Any sparse image reconstruction algorithm has improved performance using the developed measurement matrix over using random projections. Some insights into the empirical estimation of the image patch manifold are developed and several results are presented.

Citation Download Citation

Robert Muise and Dave Bottisti "Compressive imaging measurement design from an image patch manifold prior", Proc. SPIE 8399, Visual Information Processing XXI, 839905 (4 May 2012); https://doi.org/10.1117/12.919659

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KEYWORDS

Associative arrays

Reconstruction algorithms

Algorithm development

Compressed sensing

Compressive imaging

Image compression

Matrices

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