Image estimation from projective measurements using low dimensional manifolds

Johann Veras; Robert Muise

doi:10.1117/12.2053290

23 May 2014 Image estimation from projective measurements using low dimensional manifolds

Johann Veras, Robert Muise

Proceedings Volume 9109, Compressive Sensing III; 91090S (2014) https://doi.org/10.1117/12.2053290
Event: SPIE Sensing Technology + Applications, 2014, Baltimore, MD, 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. We implement a 2-way clustering then K-means algorithm to separate the estimated image space into low dimensional clusters for image reconstruction via a minimum mean square error estimator. Some insights into the empirical estimation of the image patch manifold are developed and several results are presented.

Citation Download Citation

Johann Veras and Robert Muise "Image estimation from projective measurements using low dimensional manifolds", Proc. SPIE 9109, Compressive Sensing III, 91090S (23 May 2014); https://doi.org/10.1117/12.2053290

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