Title

Image Estimation From Projective Measurements Using Low Dimensional Manifolds

Keywords

2-way clustering; Compressive Imaging; compressive measurements; K-means; minimum mean square error; sparse representation

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.

Publication Date

1-1-2014

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering

Volume

9109

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1117/12.2053290

Socpus ID

84922753557 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84922753557

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